Nov 1, 2023·2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
K Vivekrabinson, Bharath Singh Jebaraj, Rajesh Kumar S, M. Vijay · 6 authors
Real estate is an asset class traditionally associated with complex transactions and lengthy paperwork. The emergence of blockchain technology, Non-Fungible Tokens (NFTs), and smart contracts help the real estate industry transactions faster, more secure, and more transparent. This study proposes an integrated framework for revolutionizing property transactions by leveraging blockchain technology, ensuring heightened security and transparency. Here we use NFTs to represent properties which makes it easier to track changes in ownership and ensure that all parties involved in a transaction clearly understand who owns the property and when it was transferred. It also extends the application of cryptographic principles, introducing zero-knowledge proofs to validate property ownership. This innovative approach enables parties involved in property transactions to verify the authenticity of ownership without disclosing sensitive information. Through a combination of theoretical analysis, technological exploration, and real-world considerations, this paper lays the groundwork for a more secure, efficient, and privacy-aware landscape for property transactions.
In an era characterized by the rapid expansion of cloud storage services and the ubiquitous digitization of data, preserving the security and privacy of information has become paramount. This research paper explores a pioneering approach to fortify data security in cloud storage through the application of zero-knowledge proof protocols.The growing reliance on cloud storage has exposed organizations and individuals to various security threats, including data breaches, unauthorized access, and privacy concerns. Zero-knowledge proof protocols, such as zk-SNARKs and zk-STARKs, offer a unique solution to address these challenges. They allow for the verification of data integrity and authenticity without exposing the actual data, thereby ensuring data privacy in the cloud.This paper comprehensively delves into the principles and mechanisms of zero-knowledge proofs and their practical application in cloud security. It discusses the benefits of using these protocols, including enhanced data security, reduced risk of data breaches, and compliance with data protection regulations.The research also considers the challenges and limitations associated with the integration of zero-knowledge proof protocols in existing cloud infrastructures, addressing computational overhead and user adoption hurdles. Case studies and practical implementations from various organizations highlight the real-world impact and benefits of these protocols.Ethical and regulatory considerations surrounding enhanced data security and privacy are examined, emphasizing the alignment of zero-knowledge proof protocols with data protection regulations and compliance requirements.As data security continues to be a critical concern for individuals and organizations in the digital age, the findings of this research provide valuable insights into an evolving landscape where technology and privacy intersect. It encourages further research, exploration, and adoption of zero-knowledge proof protocols to ensure the integrity and confidentiality of data in cloud storage, safeguarding the digital assets of users and enterprises alike.
In the rapidly evolving domain of distributed ledger technology, scalability and interoperability have become paramount challenges for both academic and industry sectors. In this paper, we introduce a comprehensive formal model to address atomic composability across multiple rollups on Ethereum. The proposed model incorporates mechanisms like buffering, dependency management, concurrency control, and the groundbreaking zero-knowledge proofs. Moreover, we evaluate its practical repercussions, strengths, and weaknesses, ensuring resilience against manipulative or erroneous actions. The application of the proposed model to shared sequencers and other existing solutions accentuates its versatility and universality.
In today's world, secure and efficient biometric authentication is of keen importance. Traditional authentication methods are no longer considered reliable due to their susceptibility to cyber-attacks. Biometric authentication, particularly fingerprint authentication, has emerged as a promising alternative, but it raises concerns about the storage and use of biometric data, as well as centralized storage, which could make it vulnerable to cyber-attacks. In this paper, a novel blockchain-based fingerprint authentication system is proposed that integrates zk-SNARKs, which are zero-knowledge proofs that enable secure and efficient authentication without revealing sensitive biometric information. A KNN-based approach on the FVC2002, FVC2004 and FVC2006 datasets is used to generate a cancelable template for secure, faster, and robust biometric registration and authentication which is stored using the Interplanetary File System. The proposed approach provides an average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and FVC2006 datasets respectively for fingerprint authentication. Incorporation of zk-SNARK facilitates smaller proof size. Overall, the proposed method has the potential to provide a secure and efficient solution for blockchain-based identity management.
As the utilization value of personal information increases, discussions on how to provide personal information are active, but information required by institutions to utilize personal information is being exposed more than necessary. Therefore, personal privacy protection is essential to overcome the problems and limitations of personal information protection. In this study, a decentralized identity information management model that overcomes the problems and limitations of the centralized identity management method of personal information and manages and selectively provides personal information by the information owner himself and demonstrates the excellence of personal information by implementing the Smart Personal Information Provision System (SPIPS) in the PBFT consensus algorithm through experiments.
assword has been the major vulnerability and challenge in cloud computing environment due to adversarial threat sources. Zero-Knowledge Proof (ZKP) becomes the excellent optional strategy and current anticipation for Cloud Data security and assurance as it gives the server zero knowledge of passwords. However, the VERIFIER, despite having no knowledge of the password must verify that the password entered by the PROVER is correct. A major challenge that occurs is when the user disremembers the password, the cloud environment becomes inaccessible and the cloud service provider has no any replica of the password as a backup. Eavesdropping/snooping is a major threat which provides full access to the cloud environment. Adding Time-based One-time Password (TOTP) will be an elucidation to password delinquency as it expires after a certain splits of seconds or minutes which would render the password unserviceable to the snooped. This research examined cloud services in relation to the security challenges faced by organizations and proposed the hybridization of the Zero-Knowledge Proof and Time- Based One-Time Password algorithms for a better security of the cloud environment.
<title>Abstract</title> Blockchain technology has gained significant attention in recent years due to its decentralized and secure nature. Ethereum a popular blockchain platform supports the execution of smart contracts and enables the storage of data on the blockchain. However Ethereum's public nature may not be suitable for scenarios where sensitive or private data needs to be stored and accessed securely. In this paper we present GANACHE an efficient private Ethereum blockchain tool that addresses the confidentiality and security concerns associated with data storage. GANACHE leverages various cryptographic techniques such as encryption and zero-knowledge proofs to ensure the privacy of stored data while maintaining the benefits of blockchain technology.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Many techniques used in this text will require some knowledge of various levels of Calculus, especially multivariable Calculus. This chapter provides a refresher of relevant Calculus topics and may either be read carefully, skimmed, or visited as needed. Certainly the authors can work in the algebraic completion of the reals, namely do our analysis in Cn, or even a more abstract space; but as Optimization very often involves applications to real-world problems, they choose to do our work in this text in Rn. Though the result of the Intermediate Value Theorem seems intuitively obvious, its proof relies on the Completeness Property of the Real Numbers and can be found in a beginning Real Analysis text. The authors may use the ideas to find approximations of zeros of a function.
With an increasing focus on data security in today's computer systems, homomorphic encryption and zero-knowledge proofs are becoming widely used tools in privacy-preserving computing. Number theoretic transform (NTT) is a key primitive that dominates the performance of these algorithms, and thus becomes an attractive target for domain-specific acceleration. Prior NTT accelerators mostly support only fixed and small NTT sizes, which are insufficient for the diverse parameter requirements of different cryptographic algorithms and applications. In this paper, we propose an FPGA-based, scalable NTT accelerator that uses multi-dimensional decomposition to efficiently support various NTT sizes. The hardware uses a limited and fixed amount of compute and storage resources on-chip. An arbitrary-sized NTT task is decomposed into fixed-sized small NTT kernels that match the on-chip hardware and thus execute with high efficiency. We further incorporate techniques to optimize both off-chip and on-chip data transfers under such complicated decomposed execution. Overall, our accelerator balances between on-chip compute throughput and off-chip memory bandwidth. It can flexibly scale to very large NTT tasks, and outperforms prior FPGA-based NTT accelerators by over 2× at these large sizes.
Zengpeng Li, Mei Wang, Teik Guan Tan, Jianying Zhou
A trustworthy source of randomness is a crucial component of many decentralized and crypto-based application systems, especially blockchain consensus. A decentralized random beacon (DRB) periodically outputs a new source of randomness generated using a distributed technique, such as publicly verifiable secret sharing (PVSS) or distributed verifiable random functions (VRFs). These protocols offer a variety of efficiency versus randomness quality tradeoffs, but guarantee security under a variety of configurations, assumptions, and adversarial models. This article aims to provide a future-proof unbiased decentralized randomness (abbreviated as Funder) via a post-quantum threshold VRF for sustainable proof-of-stake blockchain. We also provide a generic compiler for achieving post-quantum VRF from a classical VRF solution, but our approach makes use of symmetric-key primitives Our novel compiler is validated and evaluated using the ZKBoo and ZKB++ quantum-secure zero-knowledge systems, respectively. The implementation of the proof-of-concept demonstrates that the overheads introduced by our solution are acceptable for real-world deployments even in the present day. In addition, we demonstrate the protocol’s possible application in lottery-based proof-of-stake consensus protocols.
Identity theft is one of the fastest-growing forms of cybercrime, driven by large-scale data breaches, phishing, and increasingly sophisticated impersonation attacks. Traditional identity verification methods such as passwords, PINs, and physical documents have proven inadequate in ensuring security at scale. Artificial Intelligence (AI) has emerged as a transformative enabler of next-generation identity verification by leveraging multimodal techniques, including facial recognition, voice biometrics, and document authentication. The paper discusses how AI- based verification systems can be used to prevent identity theft and how the system is used in real-time adaptive, and frictionless authentication over high-stakes areas, including banking, healthcare, e-commerce, and government services. We introduce a multi-layered verification system that combines the facial, voice and document verification modules in a single decision layer to minimize the false positives and negative but enhances the system resistance to spoofing and adversarial attacks. Practical implementations, advantages and governance are described using case studies of financial institutions, e-commerce websites and national identity programs. Nevertheless, there are still obstacles, such as demographic bias, privacy risks, adversarial vulnerability and lack of a coherent regulatory framework that makes it difficult to achieve mass adoption. In the future, we will address future directions in the area of decentralized identity, federated learning, zero-knowledge proofs, explainable AI, and international regulatory alignment. These innovations will work towards building trust, fairness and interoperability in digital identity ecosystems. Finally, this paper shows that AI-based identity verification is not merely a technological breakthrough but one of the essential needs to protect individuals, organizations, and governments against identity theft during the digital age.
Blockchain technology has gained significant attention in recent years due to its decentralized and immutable nature, and has made itself ideal for various applications such as finance, supply chain management, and healthcare. However, privacy and trust issues have emerged as major challenges in blockchain-based systems/applications. This work provides a detailed explanation of the existing privacy-preserving and trust-building techniques in blockchain-based systems. The authors begin by discussing the fundamental concepts of blockchain technology and its features that affect privacy and trust. Further, they discuss the privacy challenges faced by blockchain systems. Various privacy-preserving techniques, such as encryption, zero-knowledge proofs, and mix-zones, are explained in detail (with highlighting their strengths and limitations). In addition to privacy, trust is an important aspect of blockchain-based systems.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Verifiable Credential (VC) is a new standard proposed by the W3C association to facilitate the expression and verification of third-party-verified credentials on the Internet, such as passports or diplomas. However, the current VC data model lacks an explicit revocation design that guarantees the secure operations of the system, which limits its application. In this paper, we specify the requirements for a tamper-evident and privacy-preserving revocation mechanism, based on which we compare existing solutions and propose our revocation mechanism that satisfies all the requirements. Our design combines a cryptographic accumulator and a role-based blockchain. With zero-knowledge proof, the verifier can operate off-chain computation of the revocation status while ensuring the correctness of revocation information published on the blockchain. Our analysis shows that the proposed revocation mechanism can prevent fraud using forged and revoked credentials and relieve privacy concerns caused by the correlation of digital data. Our proof-of-concept implementation demonstrates that our revocation mechanism adds only 42.86 ms overhead in the presentation and 31.36 ms overhead in the verification of verifiable credentials. We also provide scalability analysis, which illustrates that the throughput of our blockchain can meet real-world needs.
Redactable Signature Schemes and Zero-Knowledge Proofs are two radically different approaches to enable privacy. This paper analyses their merits and drawbacks when applied to decentralized identity system. Redactable Signatures, though competitively quick and compact, are not as expressive as zero-knowledge proofs and do not provide the same level of privacy. On the other hand, zero-knowledge proofs can be much faster but some protocols require a trusted set-up. We conclude that given the benefits and drawbacks, redactable signatures are more appropriate at an earlier stage and zero-knowledge proofs are more appropriate at a later stage for decentralized identity systems
Signatures post-quantiques à partir de techniques de calcul multipartite Le développement actuel des ordinateurs quantiques pousse la communauté cryptographique à mettre au point de nouveaux cryptosystèmes dont la sécurité se fonde sur la difficulté à résoudre des problèmes cryptographiques résistant au calcul quantique. Dans le cadre de cette thèse, nous nous sommes focalisés sur la conception de schémas de signatures électroniques construits à partir de preuves à divulgation nulle de connaissance (zero-knowledge proofs of knowledge). Plus précisément, nous nous sommes intéressés au paradigme “MPC-in-the-Head” (littéralement, “calcul-multipartite-dans-la-tête”) qui fournit une méthode générique de construire de telles preuves en utilisant des techniques de calcul multipartite sécurisé. Nous proposons plusieurs nouveaux schémas de signatures utilisant le paradigme “MPC-in-the-Head”. La plupart d’entre eux sont compétitifs avec les schémas existants dans l’état de l’art post-quantique. Ils produisent des signatures ayant des tailles entre 5 et 20 kylo-octets (pour un niveau de sécurité de 128 bits) et possèdent de très petites clés (de moins de 200 octets). Les problèmes difficiles sur lesquels la sécurité de ces schémas se fonde sont très variés. Certains schémas s’appuient sur des hypothèses de sécurité issues de la théorie des codes correcteurs d’erreurs, telle que celle sur la difficulté à résoudre le problème de décodage par syndrome pour des codes linéaires aléatoires. Les autres schémas s’appuient sur la difficultés à résoudre un système d’équations quadratiques, le problème de la somme de sous-ensembles ou le problème MinRank. Nous avons également mis au point deux nouvelles techniques de MPC-in-the-Head. La première vise à gérer efficacement les situations où le secret est de petite taille avec un grand modulus. La seconde consiste en une nouvelle méthode pour transformer un protocole de calcul multipartite en preuve de divulgation nulle de connaissance. Cette nouvelle transformation offre des nouveaux compromis entre coût de communication et temps de calcul. En particulier, elle permet de produire des algorithmes de vérification très rapides. Plusieurs soumissions à l’appel du NIST pour des schémas de signatures post-quantiques supplémentaires s'appuient (parfois partiellement) sur des idées développées dans le cadre de cette thèse.
As electric vehicles become more popular, battery swap stations are gaining attention as a new type of charging facility. However, the charging process for electric vehicles involves privacy information such as user location and charging mode, which can be easily stolen or leaked, posing security risks and personal privacy concerns for users. Therefore, protecting the privacy of electric vehicle battery swap station users has become an important issue. This paper aims to study a privacy protection system for electric vehicle battery swap stations using blockchain technology. First, the basic principles and application scenarios of blockchain technology are introduced. Second, potential privacy leaks in electric vehicle battery swap stations are analysed, and a privacy protection scheme based on blockchain is proposed, including anonymous identity authentication, zero-knowledge proof, and encrypted communication. Third, a blockchain-based privacy protection system for electric vehicle battery swap stations is designed and implemented, and its performance is experimentally evaluated and compared with traditional privacy protection schemes in terms of security and efficiency. This paper demonstrates that the blockchain-based privacy protection scheme for electric vehicle battery swap stations possesses high levels of security and reliability, effectively safeguarding users' privacy information. Furthermore, this scheme exhibits promising application prospects and potential for widespread adoption. With the continuous development and utilization of blockchain technology, the privacy protection scheme for electric vehicle battery swap stations using blockchain is expected to provide users with more secure, reliable, and convenient charging services.
Federated learning, a distributed machine learning method, trains statistical models over remote devices or servers with local data, without exchanging data samples. It preserves patient data securely behind hospital firewalls, sharing only anonymous model parameters, enhancing privacy and security. This innovative approach exchanges only untraceable learned feature representations. This study introduces a new federated learning framework an innovative aggregation methods based on Particle Swarm Optimization (PSO) algorithm for breast cancer prognosis. To safeguard patient data privacy, a privacy mechanism is implemented at the user's end. By utilizing the federated learning model, they've addressed the data scarcity problem, leading to improved accuracy in breast cancer progno-sis. Federated learning, as mentioned earlier, protects personal data by sending model parameters to agents instead of raw data to a central node, keeping data localized. However, the risk of malicious nodes injecting fake data into the global model is a concern. To address this, we need a verification mechanism to authenticate senders and their training data while preserving data privacy. We use zero-knowledge proof (ZKP) for verification without exposing raw data for this part. At the outset of the federated learning process, each client computes the Merkle tree root hash of their local training data and submits it to the federated learning contract for participation. In subsequent training rounds, the client's compiler checks the integrity of their data by comparing the Merkle tree root hash to the initial one. If they match, a trace file is generated and sent for proof generation, verification, fact creation, and registration. Leveraging the TCGAbiolinks package, we conducted experiments demonstrating the effectiveness of our approach with real data. Learner clients enhance their local models using a PSO algorithm, achieving remarkable results in binary classification tasks with 400 features while preserving patient data privacy via ZKP integration. Our framework reaches 97% accuracy with eight clients, compared to a comparable system's 82%, highlighting its practical applicability and efficiency in real-world scenarios, particularly when dealing with larger numbers of participants.
Electronic health records (EHRs) based on the Internet of Things (IoT) can provide real-time health data for quick intelligent medical services and give convenience to many data-sharing scenarios. However, EHRs also face various security threats since they are highly private. To the best of our knowledge, no recognized data-sharing work can satisfy the stringent privacy requirements of EHRs. Motivated by this, we propose a blockchain-based personalized access control EHR-sharing scheme with data verifiability, which can safeguard the interests of data owners (DOs) and users simultaneously. First, we take ciphertext-policy attribute-based encryption to achieve personalized access control for DOs. Second, we design an interactive zero-knowledge proof protocol between DOs and users, which can provide authenticity verification of EHR for users and prevent EHR away from forgery. In addition, smart contracts and the interplanetary file system are used to reduce the computation and storage costs of patients. Finally, the security analysis shows that the proposed scheme meets the predefined security goals. The performance analysis demonstrates that the proposed scheme is efficient and can be applied to practical electronic medical record sharing scenarios.
Rafiqullah, Amjad Mehmood, Muhammad Altaf Khan, Carsten Maple · 5 authors
The growing deployment of Industrial Internet of Things (IIoTs) raises significant authentication concerns, especially in scenarios where resource-constrained devices need to delegate tasks with more powerful entities. This critical factor affects the information exchange during the delegation process between the Original User (OU) and the Proxy Signer (PS). The process of delegation communication entails the transfer of signing authority from a delegator to a PS. It enables the proxy signer to sign messages on behalf of delegator. As a result, complexity is increased significantly to enhance both the security and efficiency of communication overhead. To address these challenges in a transparent way, this article proposed and explores the utilization of Zero-Knowledge Proofs (ZKPs) as an additional authentication step in certificateless proxy signature mechanism. Whereas, for security hardness, Hyperelliptic Curve (HEC) is being amalgamated using ‘Schnorr signature’. Performance analysis depicts that the proposed approach exhibits not only minimized communication overhead but also provides optimal security as compared to existing approaches.
Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Simultaneously, the growth of communication networks has facilitated the efficient collection of large-scale training data. Traditional centralized machine learning, however, requires collecting data from users, raising significant concerns about privacy and security. Decentralized approaches, where participants exchange computation results instead of raw private data, mitigate these risks but introduce challenges related to trust and verifiability. A critical issue arises: How can one ensure the integrity and validity of computation results shared by other participants? Existing survey articles predominantly address security and privacy concerns in decentralized machine learning, whereas this survey uniquely highlights the emerging issue of verifiability. Recognizing the critical role of zero-knowledge proofs in ensuring verifiability, we present a comprehensive review of Zero-Knowledge Proof-based Verifiable Machine Learning (ZKP-VML). To clarify the research problem, we present a definition of ZKP-VML consisting of four algorithms and several key security properties. In addition, we provide an overview of the current research landscape by systematically organizing the research timeline and categorizing existing schemes based on their security properties. Furthermore, through an in-depth analysis of each existing scheme, we summarize their technical contributions and optimization strategies, aiming to uncover common design principles underlying ZKP-VML schemes. Building on the reviews and analysis presented, we identify current research challenges and suggest future research directions. To the best of our knowledge, this is the most comprehensive survey to date on verifiable decentralized machine learning and ZKP-VML.
Anonymity forms the basis of decentralized ecosystems, leading to an increase in criminal activities such as money laundering and illegal currency trading. Especially in blockchain-based metaverse services, activities such as preventing sexual crimes and verifying the identity of adults are becoming essential. Therefore, avatar authentication and the KYC (Know Your Customer) process have become crucial elements. This paper proposes a mechanism to achieve the KYC process by verifying user identity using smart contracts. Users obtain an SBT (Soul Bound Token) from the metaverse service provider through the DID (Decentralized Identity) credential issued during the KYC process. The identity verification of avatars occurs within smart contracts, ensuring user privacy and protection through ZKP (Zero Knowledge Proof). Tools for generating ZKP are also provided, enabling users, even those who are unfamiliar with ZKP, to use them conveniently. Additionally, an integrated wallet is offered to seamlessly manage DID credentials and SBTs. Furthermore, in case of avatar identity issues, users can request an audit by the issuer through the associated DID tokens.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques