What Scientists Have to Say (Part 2)? Recent Papers on Detecting Fake Content Using Blockchain (with Abstracts)

There are several publications and papers that explore the theme of using blockchain solutions for authenticating human-created content or related topics. Here are a few of them:

1. Combat Fake Content using Blockchain and Collective Signature Technologies

The rise of social media and the internet has reshaped our understanding of reality, influencing various aspects of our lives including politics, economics, and relationships. This is largely due to the proliferation of misinformation and disinformation spread by both individuals and organizations. The rapid advancement of Machine Learning (ML) and Deep Learning (DL) technologies has further exacerbated this issue, enabling the creation of highly convincing fake digital content. Additionally, the ability to tailor content feeds and create echo chambers amplifies this problem. To address these challenges, this paper introduces TRUSTD, a blockchain and collective signature-based ecosystem designed to authenticate content and empower users to assess its credibility. Through community validation, TRUSTD aims to combat the dissemination of fake content and promote trustworthiness in digital information (Z. Jaroucheh; M. Alissa; W. J. Buchanan; X. Liu - 2020).

Abstract: The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called "filter-bubbles" leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolves some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents (Z. Jaroucheh; M. Alissa; W. J. Buchanan; X. Liu - 2020).

2. Blockchain Technology: Redefining Trust for Digital Certificates

University certificates play a crucial role in shaping individuals' career prospects and enabling employers to assess candidates' qualifications. However, existing certification systems suffer from inefficiencies, complexities, and susceptibility to fraud. The Blockcerts project, initiated by MIT and Learning Machine in 2016, offers a promising solution by leveraging Blockchain technology to issue tamper-proof digital certificates. By providing a secure and convenient platform for certificate issuance, Blockcerts ensures the authenticity and permanence of academic records. The University of Rome “Tor Vergata” has embraced this technology through a pilot program, aiming to streamline the diploma issuance process and enhance certificate security. This paper provides a technical overview of the Blockcerts framework and evaluates its potential impact on certificate issuance practices, advocating for broader adoption of Blockchain technology in the education sector (G. Capece; N. L. Ghiron; F. Pasquale - 2020).

Abstract: University certificates can have a significant impact on people’s lives: they can help people get the job they want or allow companies to decide whether a candidate for a job has the appropriate skills. Despite their important social role, current systems for certifying academic achievements are slow, complicated, expensive, and vulnerable to forgery. In the education field, the Blockcerts project, an open source project launched by MIT and Learning Machine in 2016, seems to have the potential to become a new standard for issuing certificates using the Blockchain technology as a platform. It enables students to quickly and easily get a verifiable, tamper-proof version of their diploma. Additionally, the infrastructure provides permanence, convenience, and a level of security appropriate to the importance of the record, guaranteeing the legitimacy of the diploma. The University of Rome “Tor Vergata” started a pilot program in 2018 adopting the Blockcerts framework to issue digital diplomas. In this paper, we describe the whole process from a technical perspective and analyze the impact that a broad adoption of the Blockcerts standard might have, as compared to the current way of issuing diplomas at the University of “Tor Vergata”. Our aim is that our work might contribute to building momentum for the application of the Blockchain technology to digital certificates and stimulate further discussion with other institutions to fully exploit the potential of the technology (G. Capece; N. L. Ghiron; F. Pasquale - 2020).

3. Decentralized Name-based Security for Content Distribution Using Blockchains

In the realm of Information-Centric Networking (ICN), the use of user, content, and device names as security measures has garnered significant interest. This paper introduces a novel approach that leverages Hierarchical Identity Based Encryption (HIBE) to establish secure content distribution mechanisms based on content names. Unlike traditional methods, each user in our system maintains their own Private Key Generator, eliminating the key escrow issue common in similar solutions. Furthermore, we utilize blockchains to distribute system parameters in a decentralized manner, ensuring transparency and reliability in content owner authentication (N. Fotiou; G. C. Polyzos - 2016).

Abstract: User, content, and device names as a security primitive have been an attractive approach especially in the context of Information-Centric Networking (ICN) architectures. We leverage Hierarchical Identity Based Encryption (HIBE) to build (content) name-based security mechanisms used for securely distributing content. In contrast to similar approaches, in our system each user maintains his own Private Key Generator used for generating the master secret key and the public system parameters required by the HIBE algorithm. This way our system does not suffer from the key escrow problem, which is inherent in many similar solutions. In order to disseminate the system parameters of a content owner in a fully distributed way, we use blockchains, a distributed, community managed, global list of transactions (N. Fotiou; G. C. Polyzos - 2016).

Overall, detecting fake content using blockchain is crucial for preserving trust, protecting against manipulation, combatting malicious actors, empowering users, and promoting accountability in the digital age. As the prevalence of fake content continues to rise, blockchain-based solutions offer promising avenues for addressing this pressing challenge and fostering a more trustworthy and reliable digital environment.

Unveiling Zcash (ZEC): Exploring the Privacy-Centric Cryptocurrency

 

In the dynamic world of cryptocurrencies, where transparency and privacy often collide, Zcash (ZEC) stands out as a pioneer in prioritizing user privacy. Created in 2016 by a team of cryptographers and scientists, Zcash offers users the ability to transact privately using zero-knowledge proofs (ZKPs), a cutting-edge cryptographic technique. However, despite its innovative approach to privacy, Zcash has faced scrutiny and even been banned from some exchanges due to concerns about its privacy features.

Understanding Zcash's Privacy Features

At the heart of Zcash lies its unique privacy protocol, which allows users to shield their transaction details from prying eyes while still maintaining the integrity of the blockchain. This is achieved through the use of ZKPs, which enable users to prove the validity of a transaction without revealing any sensitive information, such as the sender, recipient, or transaction amount.

The Debate Surrounding Privacy and Regulation

While Zcash's privacy features have garnered praise from privacy advocates, they have also raised concerns among regulators and exchanges. Some argue that the anonymity provided by Zcash could facilitate illicit activities, such as money laundering or terrorist financing, by enabling users to transact anonymously. As a result, several exchanges have chosen to delist or limit trading of Zcash and other privacy-centric cryptocurrencies to comply with regulatory requirements.

Zcash's Blockchain and Development Community

Zcash operates on its own blockchain, which is based on the Bitcoin codebase but incorporates additional privacy features, including ZKPs. The project is supported by the Electric Coin Company (ECC), a team of developers and researchers dedicated to advancing the principles of privacy and decentralization. Additionally, Zcash has a vibrant community of users, developers, and enthusiasts who contribute to its ongoing development and adoption.

Why ZEC is Different From Other Cryptocurrencies?

Zcash (ZEC) differentiates itself from other cryptocurrencies primarily through its focus on privacy and anonymity. Here are some key ways in which Zcash stands out:

1. Privacy Features: Zcash utilizes advanced cryptographic techniques, specifically zero-knowledge proofs (ZKPs), to enable fully private transactions. Unlike transparent cryptocurrencies like Bitcoin, where transaction details are visible to anyone on the blockchain, Zcash transactions can be shielded, allowing users to send and receive funds without revealing their addresses or transaction amounts.

2. Optional Privacy: One unique aspect of Zcash is its opt-in privacy model. While Zcash offers the option for fully shielded transactions, users also have the choice to conduct transparent transactions similar to Bitcoin. This flexibility allows users to tailor their privacy preferences according to their needs and use cases.

3. Enhanced Security: The use of ZKPs in Zcash provides enhanced security and privacy guarantees compared to other cryptocurrencies. With zero-knowledge proofs, users can prove the validity of transactions without disclosing sensitive information, reducing the risk of privacy breaches or transaction censorship.

4. Governance and Development: Zcash has a distinct governance and funding mechanism designed to support its ongoing development and sustainability. A portion of newly minted ZEC coins goes towards funding the Electric Coin Company (ECC), the entity responsible for Zcash's development. Additionally, the Zcash community has established the Zcash Foundation, a non-profit organization focused on advancing privacy technology and supporting the Zcash ecosystem.

5. Regulatory Considerations: Due to its privacy features, Zcash has faced regulatory scrutiny in some jurisdictions. While privacy is valued by users seeking financial confidentiality, regulators have expressed concerns about its potential use in illicit activities. As a result, Zcash and other privacy-centric cryptocurrencies may face regulatory challenges and restrictions in certain regions.

Conclusion: Navigating the Future of Privacy-Centric Cryptocurrencies

As the cryptocurrency landscape continues to evolve, the debate surrounding privacy and regulation will likely persist. While Zcash and other privacy-centric cryptocurrencies offer innovative solutions to privacy concerns, they also face regulatory challenges that could impact their adoption and accessibility. Nonetheless, the development of privacy-enhancing technologies like ZKPs represents a significant step forward in empowering users to reclaim control over their financial privacy in an increasingly digital world.

In conclusion, Zcash remains at the forefront of the privacy-centric cryptocurrency movement, offering users a powerful tool to protect their financial privacy. However, its journey is not without challenges, as it navigates the complex interplay between privacy, regulation, and innovation in the cryptocurrency space. As the industry continues to mature, Zcash and projects like it will play a pivotal role in shaping the future of digital finance and privacy.

Exploring Blockchain Solutions for Authenticating Human-Created Content

 

Understanding whether content was created by a human or by AI can be important for several reasons:

Transparency and Trustworthiness: Knowing the origin of content can help readers evaluate its credibility and trustworthiness. Human-authored content may be perceived as more trustworthy because it reflects personal experiences, expertise, and perspectives, whereas AI-generated content might lack authenticity or bias.

Accountability: Attribution of content to its creators fosters accountability. Human authors can be held responsible for the accuracy and ethical implications of their work, while the accountability for AI-generated content may lie with the developers or organizations behind the AI.

Legal and Ethical Considerations: In some contexts, there may be legal or ethical implications associated with content creation. For example, plagiarism, copyright infringement, and intellectual property rights may vary depending on whether the content was created by a human or by AI.

Quality Assurance: Understanding the source of content is crucial for quality assurance purposes. Human editors and reviewers may need to assess content for accuracy, relevance, and appropriateness, which can be influenced by whether it was authored by a human or generated by AI.

User Experience: Consumers of content may have different expectations and preferences depending on its origin. For example, readers may seek out human-authored content for its unique voice, creativity, and emotional resonance, while AI-generated content may be valued for its efficiency and scalability.

As of now, there isn't a foolproof method to determine whether an article or post was written by a human or generated by an AI. However, there are some clues you can look for:

Consistency and coherence: Human-written content tends to have more consistent logic and flow, whereas AI-generated content might sometimes lack coherence or contain nonsensical passages.

Complexity: AI-generated content may struggle with generating nuanced or deeply insightful perspectives, especially on complex or abstract topics. Human writers often bring personal experiences and emotions into their writing that can be challenging for AI to replicate convincingly.

Language errors: AI-generated content may contain unusual grammar structures, awkward phrasing, or language errors that humans are less likely to make.

Author information: If you have access to information about the author, such as their background or writing history, it might provide clues. For example, if the author has a history of generating AI content, it's more likely that the piece was written by an AI.

Style and tone: Humans have distinct writing styles and tones influenced by their personalities, backgrounds, and cultures. While AI models can mimic certain styles, they may lack the depth and authenticity of human expression.

Keep in mind that these are general guidelines, and there's always the possibility of human-authored content exhibiting characteristics typically associated with AI-generated text, and vice versa. As AI technology advances, distinguishing between human and AI-generated content may become increasingly challenging.

So what is the solution?

A blockchain technology could be used to archive human-written content, providing a decentralized and immutable record of its creation. Here's how you could implement it:

Content Verification: When a human creates a piece of content, they could timestamp it and record its unique identifier (like a hash) on the blockchain. This timestamped record would serve as proof of the content's existence at a particular point in time.

Digital Signatures: Authors could digitally sign their content using cryptographic techniques, such as asymmetric encryption. The signature could then be stored on the blockchain along with the content's hash, providing a verifiable link between the author and the content.

Decentralized Storage: Rather than storing the actual content on the blockchain (which could be inefficient and costly), you could store it in decentralized storage systems like IPFS (InterPlanetary File System) or Filecoin. The blockchain would store references to the content's location and metadata.

Community Verification: You could implement a system where the community or a group of trusted validators verifies the authenticity of content before it's recorded on the blockchain. This could involve manual review, automated checks, or a combination of both.

However, ensuring that only human-generated content is recorded on the blockchain presents some challenges:

Content Verification: It may be difficult to verify whether content was truly generated by a human or an AI. While digital signatures can confirm the author's identity, they can't guarantee that the content wasn't generated with AI assistance.

Trusted Sources: You could establish criteria for who can submit content to be recorded on the blockchain, such as requiring content to come from reputable publishers or verified authors. However, this may limit the inclusivity of the archive and introduce subjective bias.

Algorithmic Detection: Develop algorithms or AI models specifically designed to detect AI-generated content. While this approach is still in its early stages, advancements in natural language processing and machine learning could improve the accuracy of such detection methods over time.

Ultimately, a combination of technical measures, community oversight, and ongoing research into content verification methods may help mitigate the risk of AI-generated content being recorded as human-authored on a blockchain-based archive.

As of now, there isn't a widely known blockchain specifically dedicated to archiving human-written content and verifying its authenticity. However, several initiatives are exploring similar concepts or aspects of content verification using blockchain technology.

One example is the use of blockchain in academic publishing to verify the authenticity and ownership of research papers. Projects like the Digital Object Identifier (DOI) blockchain initiative aim to create a decentralized registry of scholarly publications, ensuring their integrity and attribution to the correct authors.

Speaking about literary works - there are a lot of posts bragging about using AI to write books in a matter of minutes already. While blockchain technology could potentially be used to timestamp and store information about literary works, sadly, verifying their authorship as human-generated would still require human expertise and context.

Zero-Knowledge Proofs in Verifying Social Media Accounts and Posts

 

Zero-Knowledge Proofs (ZKPs) hold promise in verifying social media accounts and posts, potentially contributing to mitigating issues like fake news and fake profiles. Here's an overview of the concepts and possibilities:

Verifying Social Media Accounts and Posts:

Account Verification: ZKPs could be employed to verify the authenticity of social media accounts without revealing unnecessary personal information. Users could prove ownership of an account without disclosing identifiable details.

Post Integrity: ZKPs could ensure the integrity of posts, allowing users to prove that a post originated from a legitimate source without revealing the source's identity.

ZKP and Blockchain for Combating Fake News and Profiles:

Immutable Records: Blockchain technology can provide an immutable ledger for recording verified information about social media accounts and posts. ZKPs can be utilized to verify the authenticity of these records without compromising privacy.

Decentralized Verification: By combining ZKPs with blockchain, a decentralized system for verifying social media content can be created, reducing reliance on centralized platforms and enhancing trust.

Trust and Transparency: Blockchain and ZKPs can foster trust and transparency in social media by enabling users to independently verify the authenticity of information without relying solely on platform providers.

Social Media Platforms Utilizing ZKP Technology:

Sadly, there isn't widespread adoption of ZKP technology in mainstream social media platforms. However, smaller projects and initiatives may be exploring its potential for enhancing security and privacy in social media interactions.

In the early stages of Zero-Knowledge Proof (ZKP) concepts in social media, smaller projects and initiatives may be exploring various possibilities for leveraging this technology to enhance security, privacy, and trust in online interactions. Here are some potential initiatives that could be in the early stages:

1. Proof of Identity: Projects may be experimenting with ZKPs to enable users to prove their identity on social media platforms without revealing sensitive personal information. This could involve verifying attributes such as age, location, or membership in a particular group without disclosing unnecessary details.

2. Content Verification: Initiatives might be exploring the use of ZKPs to verify the authenticity and integrity of content shared on social media platforms. This could include proving the source of a post or verifying that a piece of content has not been tampered with since its creation.

3. Decentralized Authentication: Projects may be developing decentralized authentication systems based on ZKPs and blockchain technology. These systems could allow users to authenticate themselves across multiple platforms without relying on a central authority, reducing the risk of data breaches and account takeovers.

4. Privacy-Preserving Messaging: Initiatives might be working on privacy-preserving messaging protocols that leverage ZKPs to ensure the confidentiality of communications. This could enable users to communicate securely without their messages being accessible to anyone other than the intended recipients.

5. Anti-Spam Measures: Projects may be exploring the use of ZKPs to combat spam and fake accounts on social media platforms. By requiring users to provide cryptographic proofs of their identity, platforms could make it more difficult for malicious actors to create and disseminate spam content.

6. User-Controlled Data Sharing: Initiatives might be developing tools that allow users to control how their data is shared and used on social media platforms using ZKPs. This could include mechanisms for users to selectively disclose certain attributes about themselves without revealing the entirety of their profile.

Meta (formerly Facebook) and ZKP/Blockchain Adoption:

While Meta hasn't announced specific plans for integrating ZKP and blockchain technology into its platforms, the company is continuously exploring innovative solutions to address issues like fake news and identity verification. It's conceivable that Meta, along with other tech giants, may explore ZKP and blockchain in the future as part of broader efforts to enhance security and combat misinformation.

However, there may not be explicit endorsements or critiques of ZKPs from famous individuals in the context of social media and combating fake news, these speculative viewpoints highlight the diverse perspectives that may exist within the public discourse on privacy, technology, and online security. As awareness of ZKPs and their potential applications grows, we may see more public figures weighing in on these topics in the future.

In conclusion, while ZKPs and blockchain hold promise for verifying social media content and combating fake news and profiles, widespread adoption and integration into mainstream platforms like Meta's social media ecosystem are still in the exploratory stages. As technology evolves and awareness of these solutions grows, we may see further developments in this space.

Zero-Knowledge Proofs in Real-World Anti-Money Laundering (AML) Applications

                                                        Image source: www.lb.lt

Zero-knowledge proofs (ZK-proofs) offer promising applications in enhancing anti-money laundering (AML) efforts, particularly in the financial sector. Here's how ZK-proofs could be utilized in real-world AML applications:

Privacy-Preserving Identity Verification: Traditional AML processes often require individuals to disclose sensitive personal information for identity verification, which can pose privacy risks. ZK-proofs can enable individuals to prove certain attributes about themselves (such as age, nationality, or residency) without revealing the underlying data. This ensures that only necessary information is disclosed for compliance purposes, reducing the risk of identity theft and unauthorized access to personal data.

Transaction Monitoring and Compliance Reporting: Financial institutions are required to monitor transactions for suspicious activities and report any potentially illicit behavior to regulatory authorities. ZK-proofs can be integrated into transaction monitoring systems to analyze transaction data while preserving the privacy of individual transactions. This allows for more effective detection of suspicious patterns without compromising the confidentiality of customer information.

Customer Due Diligence (CDD): ZK-proofs can streamline the customer due diligence process by enabling financial institutions to verify customer identities and assess risk levels without requiring customers to disclose sensitive information. For example, customers could provide ZK-proofs of their income, source of funds, or beneficial ownership structures without revealing detailed financial records. This reduces the burden on customers and improves the efficiency of AML compliance procedures.

Cross-Border Transactions: International transactions present unique challenges for AML compliance due to differences in regulatory requirements and data protection laws across jurisdictions. ZK-proofs can facilitate cross-border transactions by enabling parties to verify compliance with AML regulations without sharing sensitive information with each other or with intermediaries. This promotes trust and transparency in global financial transactions while ensuring compliance with regulatory requirements.

Blockchain and Cryptocurrency Compliance: With the rise of blockchain technology and cryptocurrencies, there is a growing need for AML solutions tailored to decentralized financial systems. ZK-proofs can be used to enhance the privacy and security of blockchain transactions while enabling regulatory compliance. For example, ZK-proofs can be employed to verify the origin of funds in cryptocurrency transactions without revealing the identities of transacting parties.

Overall, ZK-proofs offer significant potential for improving the effectiveness and efficiency of AML compliance in real-world applications. By combining cryptographic techniques with principles of privacy-preserving data analysis, ZK-proofs enable financial institutions to meet regulatory requirements while safeguarding customer privacy and confidentiality. As regulatory frameworks evolve and technological advancements continue, we can expect to see further integration of ZK-proofs into AML compliance processes across various industries.

Zero-Knowledge Proofs in Passwordless Authentication

 

Zero-knowledge proofs (ZK-proofs) have gained attention in the realm of passwordless authentication due to their ability to provide secure and privacy-preserving authentication mechanisms. Here's how it generally works and some insights into its applications, prototypes, adoption, pros and cons, as well as challenges:

How it works:

Initial setup: The user and the authentication server establish a shared secret during registration.

Authentication process: When the user wants to authenticate, they generate a cryptographic proof that they possess the shared secret without revealing it. This proof is based on the zero-knowledge property, meaning that the proof does not reveal any information about the secret itself.

Verification: The authentication server verifies the proof provided by the user without learning anything about the secret itself. If the proof is valid, the user is authenticated.

Prototypes:

Several prototypes and implementations of ZK-proof-based passwordless authentication exist, often utilizing cryptographic primitives like zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) or zk-STARKs (Zero-Knowledge Scalable Transparent Argument of Knowledge). However, specific details on prototypes may vary based on the cryptographic techniques employed and the specific use cases targeted.

Adoption:

While ZK-proofs for passwordless authentication are gaining interest in research and development circles, widespread adoption is still in its early stages. Some countries and companies may be exploring or piloting these solutions, but comprehensive data on global adoption is not readily available.

Pros and Cons:

Pros:

Security: ZK-proofs provide strong security guarantees, ensuring that authentication is based on possession of a secret without revealing the secret itself.

Privacy: Users can authenticate themselves without disclosing any sensitive information to the authentication server.

User experience: Eliminating passwords simplifies the authentication process and reduces the risk associated with password-based attacks.

Cons:

Complexity: Implementing ZK-proofs requires cryptographic expertise and may be more complex than traditional authentication mechanisms.

Scalability: Depending on the specific implementation, ZK-proofs may introduce computational overhead, potentially impacting scalability.

Adoption challenges: The adoption of ZK-proof-based authentication may face resistance due to the need for infrastructure changes and concerns about compatibility with existing systems.

Challenges:

Several challenges are pivotal to making ZK-proof-based passwordless authentication work globally:

Standardization: Developing standards for ZK-proof-based authentication could facilitate interoperability and promote widespread adoption.

Performance: Improving the efficiency and scalability of ZK-proof protocols is crucial for ensuring practical feasibility in real-world applications.

Trust: Building trust in ZK-proof-based authentication systems among users, organizations, and regulatory bodies is essential for widespread adoption.

Integration: Integrating ZK-proof-based authentication into existing authentication frameworks and infrastructure without disrupting usability and compatibility is a significant challenge.

Addressing these challenges will be crucial for unlocking the full potential of ZK-proof-based passwordless authentication and driving its adoption on a global scale.

ZK-proofs in Age Verification

 

Zero-knowledge proofs (ZK-proofs) can be used in age verification to prove that someone is above a certain age without revealing their exact birthdate or any other sensitive information. This is particularly useful in scenarios such as accessing age-restricted content online or purchasing age-restricted products.

The process typically involves a verifier (such as a website or service) and a prover (the individual whose age is being verified). The prover can use a ZK-proof to demonstrate that they meet the age requirement without disclosing their actual age. The verifier can then validate the proof without learning any additional information about the prover.

Several countries have shown interest in applying ZK-proofs in age verification, particularly in the context of online age verification for accessing adult content or purchasing age-restricted items. However, specific implementations and adoption may vary.

Countries such as the United States, the United Kingdom, Canada, Australia, and several European countries have shown interest in exploring the application of zero-knowledge proofs (ZK-proofs) in age verification, particularly in online environments. These countries often deal with challenges related to age verification for accessing adult content, purchasing age-restricted products, or participating in online activities with age restrictions.

However, it's important to note that while interest may exist at a governmental or regulatory level, the actual implementation and adoption of ZK-proof-based age verification systems may vary depending on legal, technical, and societal factors within each country. Some countries may prioritize other methods or regulations for age verification, while others may actively explore innovative technologies like ZK-proofs.

Scientists and researchers have developed prototypes and proof-of-concept implementations of ZK-proof-based age verification systems. These prototypes demonstrate the feasibility of using ZK-proofs for age verification in various contexts.

Researchers and developers have explored various prototypes and proof-of-concept implementations of ZK-proof-based age verification systems. Some examples include:

Cryptographic Protocols: Researchers have developed cryptographic protocols based on ZK-proofs to verify the age of users without revealing their birthdates or other personal information. These protocols typically involve interactions between the user (prover) and the age verification service (verifier), where the user proves their age using ZK-proofs.

Smart Contracts: In blockchain-based systems, smart contracts can be utilized to facilitate age verification using ZK-proofs. These smart contracts can handle the interactions between users and verifiers, ensuring that age verification is conducted in a privacy-preserving manner.

Decentralized Identity Systems: ZK-proofs can be integrated into decentralized identity systems to provide age verification capabilities. Users can generate cryptographic proofs of their age, which can be verified by relying parties without disclosing sensitive information.

Anonymous Credentials: Researchers have explored the use of anonymous credentials based on ZK-proofs for age verification. Users can obtain anonymous credentials from trusted issuers, which can be presented to verifiers to prove their age without revealing any additional information.

Zero-Knowledge Authentication: Zero-knowledge authentication schemes have been developed where users can authenticate themselves without revealing their age or other personal details. These schemes leverage ZK-proofs to establish trust between users and verifiers without disclosing sensitive information.

These prototypes and proof-of-concept implementations demonstrate the potential of ZK-proofs in age verification systems, offering privacy-preserving solutions for verifying age in various contexts, such as online platforms, age-restricted content, and e-commerce.

While blockchain technology can be used to enhance the security and decentralization of age verification systems, it is not strictly necessary for using ZK-proofs in age verification. ZK-proofs can be implemented in various ways, including centralized systems or peer-to-peer protocols, depending on the specific requirements and constraints of the application.

Which Countries Have Shown the Most Interest in Applying ZK-Proofs?

Countries that have shown interest in applying Zero-Knowledge Proofs (ZK-proofs) into their sectors, including potentially intelligence and security, include the United Kingdom, Japan, and South Korea. Various central banks and governmental bodies within these countries are studying applications of ZK-proofs, which indicates a growing interest in utilizing this cryptographic method for enhancing privacy and security in different domains.

The specific applications and intentions of countries like the United Kingdom, Japan, and South Korea regarding Zero-Knowledge Proofs (ZK-proofs) can vary, mainly depending on their objectives in enhancing security, privacy, and efficiency in digital transactions and communications. While the detailed implementations and plans might not be fully disclosed due to the sensitive nature of intelligence and security applications, we can infer some potential uses based on the general benefits and applications of ZK-proofs.

1. United Kingdom: The UK, through its governmental and financial sectors, might be interested in ZK-proofs to secure digital identities, improve the privacy of citizen data, and enable secure, confidential transactions. This could extend to intelligence operations where securing communication and data exchange without revealing unnecessary information is crucial.

2. Japan: Known for its advanced technology sector, Japan might be exploring ZK-proofs to bolster its cybersecurity infrastructure, protect intellectual property, and enhance privacy in digital services. For intelligence purposes, ZK-proofs could be used to verify the authenticity of information or credentials without exposing sensitive data.

3. South Korea: With a strong emphasis on technological innovation and digital economy, South Korea may be looking into ZK-proofs for applications in digital finance, secure voting systems, and personal data protection. In intelligence, this could mean creating systems where information can be shared and verified between agencies without revealing the content, enhancing both domestic and international cooperation securely.

ZK-proofs offer the advantage of allowing one party to prove to another that a statement is true without revealing any information beyond the validity of the statement itself. This property is particularly appealing for intelligence and security applications where information sensitivity is paramount. Applications could include:

Secure Communication: Enabling messages to be verified for authenticity and integrity without exposing any content of the communication, crucial for intelligence operations.

Identity Verification: Allowing individuals to prove their identity or credentials without revealing unnecessary personal information, enhancing operational security.

Data Sharing: Facilitating the secure exchange of information between entities (e.g., different countries' intelligence agencies) in a way that ensures only the necessary data is revealed.

Although specifics about how each country intends to implement ZK-proofs are not widely published, the general trend indicates a growing recognition of the technology's potential to enhance privacy, security, and efficiency in various domains, including intelligence and security operations.

ZK-proofs in Military: What We Know And What We Don't

The blog post on Zero-Knowledge Proofs (ZK-Proofs) in Location-Based Services (LBS) within the military context delves into how this technology can significantly enhance operational security, privacy, and efficiency. Let's explore each highlight in detail.

Operational Security

Operational security is paramount in military operations to protect the secrecy of troop movements and locations. ZK-Proofs enable soldiers to verify their presence in a specific operational zone to access classified information or systems without revealing their exact location. This minimizes the risk of sensitive data exposure to adversaries. By using ZK-Proofs, the military can ensure that only personnel within certain geographic regions can access specific data or communication channels, thus maintaining the integrity and secrecy of operations.

Access Control for Sensitive Areas

ZK-Proofs facilitate stringent access control to restricted areas without compromising individual privacy. Military personnel can prove they are within or outside certain zones without disclosing their exact position. This technology enhances base security by ensuring that only authorized individuals can access sensitive locations, while also protecting the privacy of the personnel involved.

Covert Operations

For units operating in sensitive or hostile territories, maintaining the secrecy of their location is crucial. ZK-Proofs provide a method for these units to coordinate and authenticate without risking location exposure. This is particularly valuable for units requiring access to resources or communications based on their operational region, enabling them to operate more safely and effectively in high-risk environments.

Supply Chain and Logistics

In military logistics, the timely and secure delivery of supplies is critical. ZK-Proofs can verify the location of supply units within certain regions without revealing precise locations, optimizing logistics and supply chain management securely. This ensures that supplies reach the right place at the right time, without compromising the security of the supply chain.

In the military Supply Chain and Logistics diagram, Zero-Knowledge Proofs (ZK-Proofs) could be implemented in several key areas to enhance security, privacy, and verification processes without revealing specific details. Here's how ZK-Proofs could be integrated into different parts of the diagram:

1. Procurement of Materials: ZK-Proofs can be used to verify the authenticity and compliance of materials and suppliers without revealing sensitive information about the materials or the procurement process. This ensures that only verified and compliant materials are procured, enhancing the integrity of the supply chain.

2. Inventory Management: In the inventory management process, ZK-Proofs can help verify the presence and quantity of critical supplies without exposing their exact nature or location. This is particularly useful in scenarios where the inventory consists of sensitive or classified materials.

3. Transportation of Goods: ZK-Proofs can secure the transportation process by allowing verification of the transit status and location of goods without revealing precise routes or locations. This minimizes the risk of interception or sabotage.

4. Access to Warehouses (Storage & Distribution): Access control to warehouses storing sensitive or classified materials can be managed using ZK-Proofs. Personnel can prove their authorization to access certain areas without disclosing their specific roles or the details of the materials stored.

5. Logistics Coordination: For coordinating logistics operations, ZK-Proofs can ensure that information about the movement of goods and resources is shared securely among authorized units. This allows for efficient coordination without compromising operational security.

6. Supply to Frontline Units: When supplying frontline units, ZK-Proofs can verify the delivery and receipt of supplies without revealing the exact contents or the destination units' locations. This maintains operational secrecy while ensuring units receive necessary supplies.

7. Use in Operations: Finally, in the usage of supplies in operations, ZK-Proofs can help verify that operations are conducted as planned without revealing specific operational details. This is crucial for maintaining the element of surprise and operational security.

Implementation Challenges

Implementing ZK-Proofs, especially in dynamic and complex environments like military operations, involves significant technical challenges. These include computational resources, integration with existing systems, scalability, and the management of cryptographic keys and proofs. Additionally, military personnel would require training to understand and effectively use ZK-Proof-based systems, considering the complexity of the underlying technology.

Global Military Adoption and NATO's Stance

Specific details about the adoption of ZK-Proofs by the world's militaries and NATO countries are not widely publicized, likely due to the sensitive nature of military technologies and strategies. However, NATO and its member countries have shown interest in leveraging emerging technologies, including blockchain and cryptographic solutions, to enhance operational security and efficiency. The strategic advantage provided by ZK-Proofs in maintaining operational secrecy while ensuring secure access to information and resources would be significant, aligning with NATO's objectives of technological superiority and security.

While the exact details of ZK-Proofs' use in military applications may not be publicly available, the potential benefits for operational security, access control, covert operations, and logistics management are clear. As cryptographic research and technology continue to advance, it's likely that ZK-Proofs and similar technologies will play an increasingly important role in the future of military operations, offering a sophisticated balance between security, privacy, and strategic advantage.

What Scientists Have to Say? 8 Recent Papers on ZK-proofs and their Prototypes (with Abstracts)

Here are recent papers from 2020 onwards that discuss Zero-Knowledge Proofs (ZK-proofs) and their prototypes addressing real-world problems:

1. Deploying ZKP Frameworks with Real-World Data: Challenges and Proposed Solutions - This study presents Fact Fortress, a framework for deploying zero-knowledge proofs to enhance data privacy and integrity, focusing on blockchain technology for verifying ZK proofs and tracking data access without revealing the data itself. Scalability improvements are needed despite fair performance with large datasets (Mallozzi, 2023).

Abstract: Zero-knowledge proof (ZKP) frameworks have the potential to revolutionize the handling of sensitive data in various domains. However, deploying ZKP frameworks with real-world data presents several challenges, including scalability, usability, and interoperability. This project presents Fact Fortress, an end-to-end framework for designing and deploying zero-knowledge proofs of general statements. Our solution leverages proofs of data provenance and auditable data access policies to ensure the trustworthiness of how sensitive data is handled and provide assurance of the computations that have been performed on it. ZKP is mostly associated with blockchain technology, where it enhances transaction privacy and scalability through rollups, addressing the data inherent to the blockchain. Our approach focuses on safeguarding the privacy of data external to the blockchain, with the blockchain serving as publicly auditable infrastructure to verify the validity of ZK proofs and track how data access has been granted without revealing the data itself. Additionally, our framework provides high-level abstractions that enable developers to express complex computations without worrying about the underlying arithmetic circuits and facilitates the deployment of on-chain verifiers. Although our approach demonstrated fair scalability for large datasets, there is still room for improvement, and further work is needed to enhance its scalability. By enabling on-chain verification of computation and data provenance without revealing any information about the data itself, our solution ensures the integrity of the computations on the data while preserving its privacy (Mallozzi, 2023).

2. Experimenting with Zero-Knowledge Proofs of Training - This research formulates zero-knowledge proof of training (zkPoT) and proposes a novel protocol for logistic regression, offering privacy-preserving proofs of model training with improved practicality and scalability (Garg et al., 2023).

Abstract: How can a model owner prove they trained their model according to the correct specification? More importantly, how can they do so while preserving the privacy of the underlying dataset and the final model? We study this problem and formulate the notion of zero-knowledge proof of training (zkPoT), which formalizes rigorous security guarantees that should be achieved by a privacy-preserving proof of training. While it is theoretically possible to design zkPoT for any model using generic zero-knowledge proof systems, this approach results in extremely unpractical proof generation times. Towards designing a practical solution, we propose the idea of combining techniques from MPC-in-the-head and zkSNARKs literature to strike an appropriate trade-off between proof size and proof computation time. We instantiate this idea and propose a concretely efficient, novel zkPoT protocol for logistic regression. Crucially, our protocol is streaming-friendly and does not require RAM proportional to the size of the training circuit, hence, can be done without special hardware. We expect the techniques developed in this paper to also generally be useful for designing efficient zkPoT protocols for other, more sophisticated, ML models. We implemented and benchmarked prover/verifier running times and proof sizes for training a logistic regression model using mini-batch gradient descent on a 4~GB dataset of 262,144 records with 1024 features. We divide our protocol into three phases: (1) data-independent offline phase (2) data-dependent phase that is independent of the model (3) online phase that depends both on the data and the model. The total proof size (across all three phases) is less than 10% of the data set size (<350 MB). In the online phase, the prover and verifier times are under 10 minutes and half a minute respectively, whereas in the data-dependent phase, they are close to one hour and a few seconds respectively (Garg et al., 2023).

3. PipeZK: Accelerating Zero-Knowledge Proof with a Pipelined Architecture - PipeZK is introduced as a pipelined accelerator for ZKP, achieving significant speedup in proof generation for privacy-preserving applications including blockchain (Zhang et al., 2021).

Abstract: Zero-knowledge proof (ZKP) is a promising cryptographic protocol for both computation integrity and privacy. It can be used in many privacy-preserving applications including verifiable cloud outsourcing and blockchains. The major obstacle of using ZKP in practice is its time-consuming step for proof generation, which consists of large-size polynomial computations and multi-scalar multiplications on elliptic curves. To efficiently and practically support ZKP in real-world applications, we propose PipeZK, a pipelined accelerator with two subsystems to handle the aforementioned two intensive compute tasks, respectively. The first subsystem uses a novel dataflow to decompose large kernels into smaller ones that execute on bandwidth-efficient hardware modules, with optimized off-chip memory accesses and on-chip compute resources. The second subsystem adopts a lightweight dynamic work dispatch mechanism to share the heavy processing units, with minimized resource underutilization and load imbalance. When evaluated in 28 nm, PipeZK can achieve 10x speedup on standard cryptographic benchmarks, and 5x on a widely-used cryptocurrency application, Zcash (Zhang et al., 2021).

4. MIRAGE: Succinct Arguments for Randomized Algorithms with Applications to Universal zk-SNARKs - This paper presents MIRAGE, a new zk-SNARK system for randomized algorithms, offering practical prover times and very succinct proofs for universal zk-SNARK applications (Kosba et al., 2020).

Abstract: The last few years have witnessed increasing interest in the deployment of zero-knowledge proof systems, in particular ones with succinct proofs and efficient verification (zk-SNARKs). One of the main challenges facing the wide deployment of zk-SNARKs is the requirement of a trusted key generation phase per different computation to achieve practical proving performance. Existing zero-knowledge proof systems that do not require trusted setup or have a single trusted preprocessing phase suffer from increased proof size and/or additional verification overhead. On the other other hand, although universal circuit generators for zk-SNARKs (that can eliminate the need for per-computation preprocessing) have been introduced in the literature, the performance of the prover remains far from practical for real-world applications. In this paper, we first present a new zk-SNARK system that is well-suited for randomized algorithms—in particular it does not encode randomness generation within the arithmetic circuit allowing for more practical prover times. Then, we design a universal circuit that takes as input any arithmetic circuit of a bounded number of operations as well as a possible value assignment, and performs randomized checks to verify consistency. Our universal circuit is linear in the number of operations instead of quasi-linear like other universal circuits. By applying our new zk-SNARK system to our universal circuit, we build MIRAGE, a universal zk-SNARK with very succinct proofs—the proof contains just one additional element compared to the per-circuit preprocessing state-of-the-art zk-SNARK by Groth (Eurocrypt 2016). Finally, we implement MIRAGE and experimentally evaluate its performance for different circuits and in the context of privacy-preserving smart contracts. ∗ A major part of this work was done while Ahmed Kosba was a postdoctoral scholar at UC Berkeley. † Part of this work was done while Charalampos Papamanthou was with Oasis Labs (Kosba et al., 2020).

5. Simulation-Extractable zk-SNARK With a Single Verification - The study introduces novel SE-SNARK constructions that minimize proof size and verification time while achieving non-malleability, significantly improving upon existing methods (Kim, Lee, & Oh, 2020).

Abstract: Among the zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARK), the simulation-extractable zk-SNARK (SE-SNARK) introduces a security notion of non-malleability. The existing pairing-based zk-SNARKs designed from linear encoding are known to be vulnerable to algebraic manipulation of the proof. The latest SE-SNARKs check the proof consistency by increasing the proof size and the verification cost. In particular, the number of pairings increases almost doubles due to further verification. In this article, we propose two novel SE-SNARK constructions with a single verification. The consistency check is subsumed in a single verification through employing a hash function. The proof size and verification time of the proposed SE-SNARK schemes are minimal in that it is the same as the state-of-the-art zk-SNARK without non-malleability. The proof in our SE-SNARK constructions comprises only three group elements (type III) in the QAP-based scheme and two group elements (type I) in the SAP-based scheme. The verification time in both requires only 3 pairings. The soundness of the proposed schemes is proven under the hash-algebraic knowledge (HAK) assumption and the (linear) collision-resistant hash assumption (Kim, Lee, & Oh, 2020).

6. GZKP: A GPU Accelerated Zero-Knowledge Proof System - This paper develops GZKP, an efficient GPU-accelerated ZKP system that significantly speeds up proof generation, making ZKP more practical for online applications (Ma et al., 2023).

Abstract: Zero-knowledge proof (ZKP) is a cryptographic protocol that allows one party to prove the correctness of a statement to another party without revealing any information beyond the correctness of the statement itself. It guarantees computation integrity and confidentiality, and is therefore increasingly adopted in industry for a variety of privacy-preserving applications, such as verifiable outsource computing and digital currency. A significant obstacle in using ZKP for online applications is the performance overhead of its proof generation. We develop GZKP, a GPU accelerated zero-knowledge proof system that supports different levels of security requirements and brings significant speedup toward making ZKP truly usable. For polynomial computation over a large finite field, GZKP promotes a cache-friendly memory access pattern while eliminating the costly external shuffle in existing solutions. For multi-scalar multiplication, GZKP adopts a new parallelization strategy, which aggressively combines integer elliptic curve point operations and exploits fine-grained task parallelism with load balancing for sparse integer distribution. GZKP outperforms the state-of-the-art ZKP systems by an order of magnitude, achieving up to 48.1× and 17.6× speedup with standard cryptographic benchmarks and a real-world application workload, respectively (Ma et al., 2023).

7. Zilch: A Framework for Deploying Transparent Zero-Knowledge Proofs - Zilch is introduced to simplify the deployment of verifiable computation and zero-knowledge proofs, using a MIPS-like processor model and a cross-compiler for improved usability and flexibility (Mouris & Tsoutsos, 2021).

Abstract: As cloud computing becomes more popular, research has focused on usable solutions to the problem of verifiable computation (VC), where a computationally weak device (Verifier) outsources a program execution to a powerful server (Prover) and receives guarantees that the execution was performed faithfully. A Prover can further demonstrate knowledge of a secret input that causes the Verifier’s program to satisfy certain assertions, without ever revealing which input was used. State-of-the-art Zero-Knowledge Proofs of Knowledge (ZKPK) methods encode a computation using arithmetic circuits and preserve the privacy of Prover’s inputs while attesting the integrity of program execution. Nevertheless, developing, debugging, and optimizing programs as circuits remains a daunting task, as most users are unfamiliar with this programming paradigm. In this work, we present Zilch, a framework that accelerates and simplifies the deployment of VC and ZKPK for any application transparently, i.e., without the need of trusted setup. Zilch uses traditional instruction sequences rather than static arithmetic circuits that would need to be regenerated for each different computation. Towards that end, we have implemented Z MIPS: a MIPS-like processor model that allows verifying each instruction independently and compose a proof for the execution of the target application. To foster usability, Zilch incorporates a novel cross-compiler from an object-oriented Java-like language tailored to ZKPK and optimized our Z MIPS model, as well as a powerful API that enables integration of ZKPK within existing C/C++ programs. In our experiments, we demonstrate the flexibility of Zilch using two real-life applications, and evaluate Prover and Verifier performance on a variety of benchmarks (Mouris & Tsoutsos, 2021).

8. ZPiE: Zero-knowledge Proofs in Embedded systems - This paper presents ZPiE, a library for creating ZKP applications on embedded systems, demonstrating its portability and effectiveness in resource-limited devices (Salleras & Daza, 2021).

Abstract: Zero-Knowledge Proofs (ZKPs) are cryptographic primitives allowing a party to prove to another party that the former knows some information while keeping it secret. Such a premise can lead to the development of numerous privacy-preserving protocols in different scenarios, like proving knowledge of some credentials to a server without leaking the identity of the user. Even when the applications of ZKPs were endless, they were not exploited in the wild for a couple of decades due to the fact that computing and verifying proofs was too computationally expensive. However, the advent of efficient schemes (in particular, zk-SNARKs) made this primitive to break into the scene in fields like cryptocurrencies, smart-contracts, and more recently, self-sovereign scenarios: private-by-design identity management and authentication. Nevertheless, its adoption in environments like the Internet of Things (IoT) remains unexplored due to the computational limitations of embedded systems. In this paper, we introduce ZPiE, a C library intended to create ZKP applications to be executed in embedded systems. Its main feature is portability: it can be compiled, executed, and used out-of-the-box in a wide variety of devices. Moreover, our proof-of-concept has been proved to work smoothly in different devices with limited resources, which can execute state-of-the-art ZKP authentication protocols (Salleras & Daza, 2021).

These papers illustrate the diverse applications of ZK-proofs, from blockchain privacy and secure model training to accelerating proof systems and enabling ZKP in embedded systems.

ZK-Proofs and Blockchain Technology: Can They Exist Without One Another?

Zero-Knowledge Proofs (ZK-Proofs) and blockchain technology are both significant advancements in the field of cryptography and decentralized systems. They can and do exist independently of each other, but their combination unlocks powerful capabilities for privacy and security in blockchain applications.

Can They Exist Without One Another?

ZK-Proofs are a cryptographic method that allows one party (the prover) to prove to another party (the verifier) that a statement is true, without revealing any information beyond the validity of the statement itself. This concept predates blockchain technology and can be applied in various fields beyond blockchain, such as secure voting systems, identity verification, and more.

Blockchain Technology is a distributed ledger technology that enables the creation of a decentralized, immutable record of transactions. It can function without ZK-Proofs, utilizing other forms of cryptographic verification and consensus mechanisms to maintain security and integrity.

Pros and Cons of Using Blockchain in ZK-Proofs

Pros:

1. Enhanced Privacy: ZK-Proofs can enhance the privacy features of blockchain by allowing transactions to be verified without disclosing the actual information contained in the transactions. This is particularly useful in applications requiring data privacy and confidentiality.

2. Scalability: Some ZK-Proof techniques, such as zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge), can help in making blockchains more scalable by reducing the amount of data that needs to be stored on-chain for transaction verification.

3. Interoperability: ZK-Proofs can facilitate interoperability between different blockchain systems by allowing cross-chain verifications without revealing sensitive data.

Cons:

1. Complexity: Implementing ZK-Proofs within blockchain systems can significantly increase the complexity of the system, making it more challenging to develop, audit, and maintain.

2. Performance Overhead: The computational requirements for generating and verifying ZK-Proofs can be substantial, potentially leading to performance overheads.

3. Adoption Barriers: The complexity and novelty of ZK-Proofs may pose barriers to widespread adoption and understanding among users and developers.

Types of ZK-Proofs Supporting Blockchain

Several types of ZK-Proofs are utilized in blockchain applications, including:

zk-SNARKs: Allow for the verification of transactions without revealing any transaction details, widely used in privacy-focused cryptocurrencies.

zk-STARKs: Similar to zk-SNARKs but without the need for a trusted setup, offering better scalability and quantum resistance.

Bulletproofs: Another variant of ZK-Proofs, which are more efficient in terms of space than zk-SNARKs but require more computational power for verification.

Hyperledger and ZK-Proof Case Prototype

Hyperledger is an open-source collaborative effort created to advance cross-industry blockchain technologies. It's not a single blockchain, but rather a suite of blockchain tools and frameworks aimed at different uses, including Hyperledger Fabric, Hyperledger Sawtooth, and more.

A case prototype using Hyperledger to create a ZK-Proof could involve leveraging Hyperledger Ursa, a shared cryptographic library that includes support for ZK-Proofs. For example, a company might use Hyperledger Fabric to build a private blockchain for supply chain management. By incorporating ZK-Proofs via Ursa, the company could allow participants to verify transactions (such as the transfer of goods) without revealing sensitive information about the goods, their cost, or the parties involved. This application would enhance privacy and security while maintaining the integrity and transparency of the supply chain data on the blockchain.

In summary, while ZK-Proofs and blockchain technology can exist and function independently, their integration offers unique advantages for enhancing privacy, security, and scalability in decentralized systems. However, the complexity and computational requirements of ZK-Proofs present challenges that need to be carefully managed in blockchain applications.