Federated learning has found extensive application in the multimedia domain. However, due to its distributed nature, it is vulnerable to attacks such as Byzantine poisoning. To counteract malicious attacks, the secure aggregation process in federated learning requires input validation from participants. Existing input verification schemes, such as ACORN (USENIX Security 2023), ROFL (S&P 2023), et al., efficiently assess the validity of client inputs, but they fail to account for the impact of weights and do not support weighted secure aggregation. To address these issues, we propose α-SAV, an efficient weighted input verification scheme that utilizes Pedersen commitments to encrypt both privacy and weighted gradients. Our scheme incorporates a non-interactive zero-knowledge proof, the Sigma protocol, allowing clients to generate input proofs without interacting with the server. Verified inputs can then contribute to weighted aggregation. α-SAV is highly compatible, seamlessly integrating into existing federated learning frameworks with minimal additional cost. Experimental results demonstrate that the cost of α-SAV is linear. When trained on the MNIST dataset, the client computation time for α-SAV is 1.6 seconds, resulting in only 24% additional cost compared to ACORN and 3% compared to ROFL.
Abstract The increasing levels of data collection and processing, and the consequential risks to both individual users and society, have led to rising demands for privacy. This chapter first discuses different notions of privacy, including anonymity, and how cryptography relates to them. It then reviews a range of cryptographic privacy-enhancing technologies that can be used to support a variety of privacy objectives. The first is Tor, which is used to support anonymity on the internet. Next, it is shown how zero-knowledge proofs enable truths to be communicated without revealing unnecessary information. The goal of secure multiparty computation (MPC) is to enable sensitive data from multiple sources to be input to a computation while maintaining the privacy of the component data items. Finally, a range of further technologies are introduced, including blind signature schemes, group signature schemes, homomorphic encryption, private set intersection and private information retrieval.
While Federated learning (FL) is considered privacy-preserving by nature, it remains vulnerable to many attacks, such as data and model poisoning, that compromise data integrity and model accuracy. Conventional privacy-preserving federated learning (PPFL) mechanisms, including homomorphic encryption (HE), secure aggregation, and secure multiparty computation (SMPC) demonstrate several limitations, such as high computational complexity, significant communication overhead, and scalability challenges. To overcome the aforementioned issues, we propose an end-to-end secure FL architecture that integrates differential privacy (DP), zero-knowledge proof (ZKP), and median aggregation. DP prevents data leakage during model updates by introducing Laplacian noise for privacy preservation. ZKP is implemented through Schnorr’s protocol, which enables lightweight and efficient client authentication without revealing sensitive information. Finally, median aggregation is incorporated to mitigate the impact of outliers and adversarial updates, ensuring robust prediction aggregation. The experimental results indicate that the proposed approach outperforms other well-known PPFL methods including partially homomorphic encryption (PHE), fully homomorphic encryption (FHE) and SMPC. It delivers substantial improvements in global accuracy, especially for larger client counts, with gains of 10%-30% over the other methods. The client training time is significantly reduced by 70%-90%, ensuring faster processing. The approach also excels at reducing average round latency by 80%-95%, enhancing the overall efficiency of the system. Communication overhead is significantly reduced by 65%-85%, lowering data transfer costs per round. Furthermore, the size of the model is minimized by 60%-85%, making it more resource efficient and scalable for larger deployments.
The demand for privacy-preserving machine learning has led to the rise of Federated Learning (FL), where multiple clients collaboratively train a model without sharing raw data. Despite its privacy benefits, FL is vulnerable to Byzantine failures, where malicious or faulty participants inject corrupted updates, threatening model integrity. To address this, a range of Byzantine-resilient aggregation techniques have been proposed, including statistical filters (e.g., Trimmed Mean, Krum), trust-based weighting, cryptographic protocols, and hybrid strategies. This paper presents a systematic literature review (SLR) of these defenses, evaluating their robustness, scalability, and suitability for real-world applications. Challenges such as non-IID data, adaptive attacks, and trade-offs between security and efficiency are critically examined. In addition, we explore emerging trends such as domain-specific defenses, energy-aware FL, quantum-resilient methods, and federated zero-knowledge proofs. A novel classification of hybrid approaches and a standardized benchmarking framework are proposed to guide future research. This review aims to support the development of resilient, efficient and scalable decentralized learning systems in adversarial environments.
In the rapidly evolving landscape of cloud computing, ensuring secure user authentication and protection against cyber-attacks has become increasingly critical. This research proposes a novel security framework for cloud systems based on the Quantum Zero-Knowledge Proof (ZKP) technique, aiming to provide a privacy-preserving and quantum-resilient authentication mechanism. The core of the proposed model lies in leveraging photon polarization at specific quantum angles to implement secure and non-disclosive verification, effectively allowing users (provers) to prove their identity without revealing any sensitive credentials. The system's architecture integrates a Zero Knowledge Proof Engine (ZKE), which forms the backbone of the security protocol, enhancing resilience against Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks. The quantum properties of photons enable a high level of randomness and unpredictability, significantly improving the robustness of the system. A Python-based simulation environment has been developed to model the proposed engine and conduct experimental validations. Furthermore, a web-based application interface has been designed to facilitate seamless interaction between cloud users and the authentication system, demonstrating real-time threat detection and response. Experimental results, visualized through performance metrics and interface output, confirm the effectiveness and practicality of the proposed model. This approach not only enhances security but also offers a scalable and user-friendly solution for modern cloud environments, marking a significant step toward integrating quantum principles into mainstream cybersecurity infrastructures.
This paper studies the \emph{unimodular isomorphism problem} (UIP) of convex lattice polytopes: given two convex lattice polytopes $P$ and $P'$, decide whether there exists a unimodular affine transformation mapping $P$ to $P'$. We show that UIP is graph isomorphism hard, while the polytope congruence problem and the combinatorial polytope isomorphism problem (Akutsu, 1998; Kaibel, Schwartz, 2003) were shown to be graph isomorphism complete, and both the lattice isomorphism problem ( $\mathrm{Sikiri\acute{c}}$, $\mathrm{Sch\ddot{u}rmann}$, Vallentin, 2009) and the projective/affine polytope isomorphism problem (Kaibel, Schwartz, 2003) were shown to be graph isomorphism hard. Furthermore, inspired by protocols for lattice (non-) isomorphism (Ducas, van Woerden, 2022; Haviv, Regev, 2014), we present a statistical zero-knowledge proof system for unimodular isomorphism of lattice polytopes. Finally, we propose an algorithm that given two lattice polytopes computes all unimodular affine transformations mapping one polytope to another and, in particular, decides UIP.
The rapid digital transformation being currently experienced the developing economies such as Zimbabwe has underlined the inefficiencies and vulnerabilities in security of traditional Know Your Customer (KYC) processes. These KYC processes and procedures are predominantly manual, slow and are prone to data breaches. This paper proposes a privacy preserving authentication model for KYC optimization using Zero-Knowledge Proof (ZKP) cryptography. This model addresses critical challenges which include prolonged customer onboarding times, high operational costs and data compliance risks. By means of leveraging ZKP the model enables secure identity verification without exposing sensitive data which ensures compliance with Zimbabwe Data Protection Act. A mixed-methods approach was adopted, combining qualitative and quantitative techniques to design, develop and evaluate the model. Experimental results have demonstrate significant improvements in data privacy and onboarding efficiency which has seen reduced onboarding time from 3 days to under 10 minutes. The model scalability and adaptability potential makes it suitable for diverse sectors which covers education, healthcare, e-commerce and government services therefore positioning Zimbabwe as a leader in secure digital transformation.
Blockchain technology is becoming an important tool for secure financial transactions. It supports decentralized finance (DeFi) services and new ways of auditing. This paper gives an overview of how blockchain is used in financial modeling, focusing on DeFi and auditing. We explain the basic technology behind popular blockchain systems, like public platforms such as Ethereum (with smart contracts and oracle networks), and private systems like Hyperledger Fabric. We also look at advanced methods like zero-knowledge proofs. We show how these tools help build financial models in DeFi by allowing peer-to-peer services without needing trust, and in auditing by making data more transparent and secure. We compare different blockchains in terms of speed, cost, and how well they scale. Security issues (like smart contract bugs or attacks on consensus) and practical problems (like trusting oracles and following laws) are also discussed. The review article looks at challenges in using blockchain and some of the latest solutions, such as Ethereum’s move to proof-of-stake, sharding for better scalability, and using zero-knowledge proofs for privacy. We also suggest future research topics, like connecting different blockchains, checking smart contracts with formal methods, creating better rules and laws, and training skilled workers. The goal is to help researchers and professionals understand the current situation and future of blockchain in finance and auditing.
Blockchain technology presents transformative opportunities for secure personal data sharing, particularly in healthcare, finance, and identity management. However, its widespread adoption is constrained by challenges such as limited scalability, privacy concerns, and conflicts with regulatory frameworks like the General Data Protection Regulation (GDPR). This study introduces a novel hybrid framework that integrates the InterPlanetary File System (IPFS) for off-chain storage with Zero-Knowledge Proofs (ZKPs) to enhance privacy, ensure regulatory compliance, and reduce on-chain storage demands. Employing a Design Science Research (DSR) methodology, the framework was developed and validated using Ethereum and Hyperledger Fabric, guided by insights from a systematic review of 180 studies from 2018 to 2023. Empirical evaluations revealed a 75% reduction in blockchain storage, 98% GDPR compliance, and zk-SNARK proof verification times below one second. The framework also enables GDPR-compliant erasure by removing encrypted off-chain data while preserving on-chain auditability. Despite challenges such as IPFS latency and trusted setup complexities, the solution offers a scalable and privacy-preserving architecture applicable to real-world domains, especially in privacy-critical environments like healthcare and finance by resolving blockchain’s GDPR compliance paradox.
This work proposes a Fiat-Shamir linkable ring lattice-based signature scheme, building on Yuen et al. ‘s generic ring signature. It converts the MLWE-based Σ-protocol into a linkable ring signature through the Fiat-Shamir transformation, employing rejection sampling to avoid private key leakage. Linkability is achieved by attaching a public key structure label, avoiding complex zero-knowledge proofs and pseudorandom function calculations. Compared to other lattice-based schemes, our approach improves signature length. Security analysis under the random oracle model confirms correctness, anonymity, unforgeability, linkability, and non-slanderability.
Abstract The breaches of the blockchain wallet keys greatly harm the security of blockchain transactions. To protect the secret keys, the known solutions, such as hierarchical deterministic wallets proposed in BIP32 or stealth addresses adopted in Monero, have been extensively researched. However, most of the existing works assume the key is safe, in the sense that it cannot be stolen or damaged, which is not true in practice. Moreover, current key revocation mechanisms either rely on centralized authorities, compromising decentralization, or require economic incentives to ensure nodes remain consistantly online. In this paper, we introduce Cocoon, the first blockchain wallet scheme that supports stealth addresses and provides a wallet revocation mechanism without the need for certificates. Cocoon not only ensures the privacy of wallet secret keys but also can individually revoke compromised keys with high performance. Our contributions are three-fold: First, we present the formal model and the related security definitions. Next, we give a generic construction based on the hierarchical identity-based signature, identity-based key encapsulation mechanism and non-interactive zero-knowledge proof. We then extend the scheme to the hierarchical setting for diverse scenarios. Finally, we give the implementation, and the results show that the scheme is practical.
Maintaining data authenticity, security, reliability, and integrity is challenging in decentralized storage and backup systems. Existing blockchain-based backup systems majorly depend on centralized off-chain storage or single-chain implementation, which leaves them vulnerable to security flaws, high expenses, and single points of failure. In this study, a multi-blockchain backup and restore system incorporates Zero-Knowledge Proofs (ZKPs) for data integrity verification and threshold cryptography for secure access control. Unlike traditional blockchain storage, this system distributes backup metadata across multiple blockchains (Ethereum, Hyperledger) while storing encrypted data in decentralized storage (IPFS). ZKPs provide integrity verification without revealing data, hence improving privacy and security. Also, Shamir’s Secret Sharing provides multi-party authentication for restoration, prohibiting unauthorized access. The system outperforms existing models by improving fault tolerance, privacy, and security compliance while reducing storage costs. Experimental evaluations compare the storage efficiency, retrieval speed, and security of this method against existing blockchain-based storage methods. This work proposes an innovative decentralized backup and restore system for managing security, efficiency, and redundancy, offering a robust solution for secure and verifiable data management in blockchain environments.
M. M. Rakibul Hasan, Mohammod Abul Kashem, Md Mahedi Zaman Zaber, Farjana Akther Hima
Traditional fundraising methods often struggle with issues such as limited transparency, high intermediary costs, and inefficiencies in fund distribution, leading to donor mistrust and reduced participation. This paper introduces a secure, decentralized fundraising framework that leverages Ethereum based smart contracts, Web3 wallet integration, and IPFS for decentralized storage. The proposed system eliminates the need for intermediaries, enabling automated, tamper proof transactions and real time fund tracking. Key features include KYC based fundraiser verification, zero-knowledge proof (ZKP) for privacy, and zk rollup technology for improved scalability. Performance evaluations demonstrate low transaction latency, optimized gas consumption, and successful end to end integration with blockchain APIs. The system offers a practical alternative to conventional platforms by enhancing transparency, reducing operational costs, and ensuring accountable fund distribution. This framework has the potential to transform the future of charitable giving and crowdfunding through blockchain innovation.
To cater to the needs of (Zero Knowledge) proofs for (mathematical) proofs, we describe a method to transform formal sentences in 2x2-matrices over multivariate polynomials with integer coefficients, such that usual proof-steps like modus-ponens or the substitution are easy to compute from the matrices corresponding to the terms or formulas used as arguments. By evaluating the polynomial variables in random elements of a suitably chosen finite field, the proof is replaced by a numeric sequence. Only the values corresponding to the axioms have to be computed from scratch. The values corresponding to derived formulas are computed from the values corresponding to their ancestors by applying the homomorphic properties. On such sequences, various Zero Knowledge methods can be applied.
In the evolving landscape of digital technologies, blockchain has emerged as a cornerstone for building decentralized, transparent, and tamper-proof systems. Central to the security and reliability of blockchain is the use of advanced cryptographic techniques, which ensure data integrity, user privacy, and resistance to malicious attacks. This paper provides a comprehensive analysis of cutting-edge cryptographic technologies that are shaping the future of blockchain networks. Key mechanisms discussed include zero-knowledge proofs, homomorphic encryption, ring signatures, and post-quantum cryptography. These technologies not only enhance privacy and scalability but also prepare blockchain systems to withstand future computational threats, including those posed by quantum computing. By exploring real-world implementations and potential applications, this research underscores the critical role of advanced cryptography in enabling secure, scalable, and future-ready decentralized infrastructures. The study also highlights ongoing challenges and future research directions to optimize the integration of these technologies in both public and private blockchain environments. Key Words: Blockchain, Cryptography, Zero-Knowledge Proofs, Post-Quantum Cryptography, Homomorphic Encryption, Ring Signatures, Decentralized Security, Privacy-Preserving Technologies
The cybersecurity threats targeting industrial control systems (ICS) are evolving with increasing sophistication. Addressing the detection blind spots in existing source code analysis techniques, this study reveals a dual security paradox arising from code sensitivity: privacy leakage risks caused by decompilation techniques and integrity verification deficiencies in reverse engineering. This paper investigates three critical challenges: (1) What are the component flow process and detection elements of ICS component source code? (2) How can high-performance and reliable tracing and traceability be provided for ICS component source code exceptions and routine detection? (3) How can privacy enhancement and trusted detection of ICS component source code with high sensitivity be achieved? This paper proposes a blockchain-integrated trusted detection framework for ICS (BCTD-ICS), delivering groundbreaking solutions: (1) Establishing a lifecycle circulation model that systematically maps component types, stakeholders, and detection parameters; (2) Developing a tripartite collaborative architecture (Blockchain-Identification Resolution Zero-knowledge proofs (ZKPs)), featuring a traceability mechanism with trusted identification codes (resolution efficiency: 40ms/105 queries) to eliminate decompilation-induced privacy risks; (3) Creating an industrial-oriented privacy enhancement system utilizing DBSCAN clustering for intelligent sampling (26% compression rate on BCN3D Moveo) and optimizing ZK-SNARK protocols through Shamir’s Secret Sharing, establishing a backdoor-resistant distributed parameter generation system (time delay increment < 100ms). Experimentally verified, our solution enables ICS component code detection supply-chain-wise without sensitive data leakage in real-world industries. This work establishes a novel trusted detection paradigm for ICS, advancing detection efficiency and credibility under strict privacy preservation requirements, meeting Industry 4.0 security demands.
Smart Grid Security and Resilience
Physical Unclonable Functions (PUFs) and Hardware Security
As large language models (LLMs) are used in sensitive fields, accurately verifying their computational provenance without disclosing their training datasets poses a significant challenge, particularly in regulated sectors such as healthcare, which have strict requirements for dataset use. Traditional approaches either incur substantial computational cost to fully verify the entire training process or leak unauthorized information to the verifier. Therefore, we introduce ZKPROV, a novel cryptographic framework allowing users to verify that the LLM's responses to their prompts are trained on datasets certified by the authorities that own them. Additionally, it ensures that the dataset's content is relevant to the users' queries without revealing sensitive information about the datasets or the model parameters. ZKPROV offers a unique balance between privacy and efficiency by binding training datasets, model parameters, and responses, while also attaching zero-knowledge proofs to the responses generated by the LLM to validate these claims. Our experimental results demonstrate sublinear scaling for generating and verifying these proofs, with end-to-end overhead under 3.3 seconds for models up to 8B parameters, presenting a practical solution for real-world applications. We also provide formal security guarantees, proving that our approach preserves dataset confidentiality while ensuring trustworthy dataset provenance.
Aiming at goldbach conjecture, we come up with the thought of dialectical setnumber theory, according to the axiom of Nature Number, the theorem of Number Theory,the axiom of queue of Set Theory, as well as the theorems got in exceptional approach byHua Luogeng and other mathematicians, using the given prime number method, reductionto absurdity and transfinite induction, this thesis proves the goldbach conjecture from theperspective of ordinal simplely; and also every even number larger than 6 is the sum of twodistinct primes.
Sahana Bhat, K Meeraja, Suhani Verma, Indu Radhakrishnan
VeryCert is a unified certificate management system that simplifies issuing, managing, and revoking digital certificates. As cyber threats rise and IT environments grow complex, secure authentication is essential. VeryCert provides a user-friendly platform for creating and managing certificates, ensuring access control by issuing them only after payment. The process begins with a Certificate Signing Request (CSR) submitted upon payment completion, after which the certificate is issued. For example, a valid certificate allows access to platforms like Cinezo, confirming payment. Administrators can view pending requests, approve them using a default CA or OpenSSL, and manage issued certificates with expiry tracking and revocation options. By integrating secure, verifiable payment-based authentication, VeryCert enhances security, data integrity, and compliance with industry standards.
As democratic processes increasingly transition to digital environments, safeguarding voter privacy and maintaining electoral integrity have become paramount. This study investigates the application of Zero-Knowledge Proofs (ZKPs) as a cryptographic framework for developing secure and private electronic voting systems. A comparative performance evaluation was conducted between ZKP-based voting protocols and traditional systems, focusing on key metrics such as validation time, privacy leakage index, and memory usage. Quantitative data analysis, supported by statistical methods including mean comparisons and standard deviation assessments, highlights the superiority of ZKP-based systems in minimizing information leakage while maintaining verifiability. Although ZKP protocols introduce higher memory consumption, the trade-off results in substantially enhanced voter anonymity and reduced validation latency. The findings suggest that ZKPs provide a scalable and efficient solution to the dual challenge of transparency and privacy in digital voting infrastructures. This research contributes to the growing body of work on cryptographic voting technologies and underscores the importance of balancing security with performance in the design of future e-voting systems. Keywords: Zero-Knowledge Proofs, E-voting, Cryptography, Privacy, Secure Voting Systems, Digital Democracy, Voter Anonymity, Cryptographic Protocols, Electoral Integrity, Privacy-Preserving Computation
Bhatt Vinayak Vishwanath, Nishanth Kumar Pathi, Shinu Abhi
Zero Knowledge Proof (ZKP) is a cryptographic method that allows a prover to demonstrate to a verifier that a statement is true without revealing any additional information. This is highly relevant in healthcare, where data privacy is critical. By integrating ZKP into healthcare applications, sensitive patient data can be securely verified without exposing unnecessary information. In healthcare, protecting sensitive data while enabling seamless collaboration between doctors, laboratories, and other entities is crucial. Sharing or verifying data often leads to privacy risks. The project aims to develop a ZKP protocol for a healthcare system, allowing secure and private verification of sensitive medical data between doctors and laboratories. The ZKP protocol is implemented using a hybrid system with cloud services and local machines for secure computation. AWS Key Management Service (KMS) handles encryption and decryption. The protocol creates cryptographic proof for verifiers to confirm data validity without accessing the data itself. Established cryptographic libraries and healthcare test data are used in the implementation. The system ensures scalability and security by leveraging AWS infrastructure and MySQL database integration for proof storage. The ZKP protocol successfully verified health care data without exposing sensitive information, reducing the risk of data breaches. This approach improves data security without compromising functionality, setting new privacy standards in healthcare data management.
Blockchain technology can potentially drive revolutionary changes in data security, privacy, and efficiency within healthcare industry. The architecture of blockchain and its main components are described in this chapter. It explored two main types of blockchain systems, permissionless and permissioned. Blockchain addresses challenges that have plagued healthcare sector, including siloed data, a lack of interoperability, and inefficient service delivery for years. Due to its decentralized nature, it increases healthcare ecosystem’s transparency and security. Blockchain contains features like distributed ledger, immutability, and enhanced data privacy that can change how healthcare services are delivered across the globe. The chapter focuses on healthcare, including multiple uses of blockchain in electronic health records, clinical and global data sharing, medical history management, research, clinical trials, drug supply chain processes, and billing systems. Moreover, blockchain enhances data access control and boosts operations in the pharmaceutical industry. Blockchain has a lot of advantages but encounters challenges in terms of limited scalability and controls, especially in healthcare context. New technologies like Zero-Knowledge Proof, Artificial Intelligence, and the Internet of Things will complement blockchain technology by increasing integration and working within healthcare systems to promote innovation and enhance patient outcomes.
The advent of quantum computing poses a significant threat to the cryptographic foundations of current electronic voting (e-voting) systems, which commonly rely on algorithms such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). These algorithms are believed to be vulnerable to attacks imposed by quantum computers, jeopardizing core e-voting properties, including ballot secrecy, integrity, and auditability. To address these challenges, this work presents a quantum-resistant electronic-voting (e-voting) system, named post-quantum e-voting system (PQEVS) built entirely upon cryptographic primitives standardized by the National Institute of Standards and Technology (NIST) for post-quantum security. The proposed PQEVS utilizes Dilithium for secure voter authentication, Brakerski/Fan-Vercauteren (BFV)-based Fully Homomorphic Encryption (FHE) for privacy-preserving vote tallying, and Picnic-based Zero-Knowledge Proofs (ZKPs) to ensure vote validity without compromising voter anonymity. Designed for modularity and scalability, our PQEVS delivers enhanced security while achieving significant performance gains, reducing vote processing latency by 85% and supporting throughputs of up to 36,000 votes per second. These results highlight the practicality and robustness of post-quantum cryptography in securing large-scale electoral processes, setting a new benchmark for verifiable and future-proof e-voting systems.