The transition to post-quantum cryptography poses an unprecedented challenge for Bitcoin and Ethereum, as it involves implementing a defensive downgrade that imposes immediate, severe costs with no tangible benefits. While quantum computers capable of breaking secp256k1 require between 523–2,500 logical qubits, with the author deriving 523 logical qubits as an algorithmic lower bound (not inclusive of arithmetic and ancilla qubits) for a canonical Shor/phase-estimation circuit using the formula QL = 2⌈log2(n)⌉ + 2 + ⌈log2(2 + 1/(2ε))⌉ for ε = 0.001, and conservative estimates ranging up to 2,500 logical qubits based on comprehensive resource models—significantly less than the 2,100–2,400 logical qubits es- timated for general elliptic curves—current systems achieve only ∼100 logical qubits. IBM’s quantum roadmap projects 500–1,000 logical qubits by 2029, placing the critical threshold within 4–10 years depending on which estimate proves accurate. This timeline collides with the reality that convincing decentralized communities to accept 50% capacity loss and 2– 3× fee increases may take 10–15 years in themselves, based on historical governance patterns where even beneficial upgrades required 2–5+ years. Current testnet implementations on per- missioned systems show measurable performance degradation. Critically, this data comes from fundamentally different architectures than permissionless networks, which will likely experience 30–50% additional performance degradation due to global verification requirements, heterogeneous hardware, and compounding propagation delays. This methodological limitation—extrapolating from permissioned to permissionless systems—represents a critical infrastructure failure that introduces massive uncertainty into migration planning. Com- pounding this challenge, secp256k1 is not officially approved by NIST under FIPS 186-5 or SP 800-186, creating additional regulatory vulnerabilities. Beyond transient impacts, PQC creates permanent state bloat, with quantum-resistant accounts requiring 59 times more storage (1,952 bytes / 33 bytes = 59.2× for ML-DSA-65), thereby accelerating centralization- tion. This paper presents a comprehensive framework acknowledging these harsh realities. While we propose specific BIP/EIP implementations and optimization strategies that might achieve 50–60% capacity retention, we recognize that the quantum threat timeline may now be shorter than even the minimum viable migration period. Unlike beneficial upgrades like SegWit (which took 20 months for activation and 5+ years for 50% adoption despite offering improvements), PQC migration is a purely defensive measure imposing only costs. The stark reality: blockchain communities must choose between accepting immediate emergency action or facing quantum vulnerability by 2029.
Abstract: Homomorphic encryption (HE) enables secure computations on encrypted data without decryption, offering a transformative solution for privacy-preserving computation. This review presents a ten-year retrospective (2014–2024) on HE’s evolution since Gentry’s 2009 fully homomorphic encryption (FHE) scheme, which introduced the concept of performing arbitrary computations on ciphertexts. Early schemes were hindered by inefficiencies like computational overhead and noise accumulation. Over the past decade, significant advancements have addressed these barriers. Schemes such as BGV, BFV, and CKKS have been developed for efficient integer and approximate real-number computations. Algorithmic innovations like optimized bootstrapping and improved noise management have reduced complexity. Hardware acceleration using GPUs and FPGAs has enhanced performance, while integration with secure multi-party computation and zero-knowledge proofs has broadened HE’s applicability. Applications now span privacy-preserving machine learning, genomic data analysis, and financial analytics. Toolkits such as SEAL, HElib, and PALISADE have improved accessibility for developers and researchers. Despite progress, challenges remain, including balancing efficiency and security, and improving usability for non-experts. The article also explores HE’s reliance on lattice-based problems like Learning With Errors (LWE) and Ring-LWE, which provide quantum resistance. As hybrid cryptographic models emerge, HE is increasingly recognized as a key component in securing sensitive data in the postquantum era. This review highlights HE’s maturation from a theoretical concept to a practical solution, demonstrating its potential as a cornerstone for secure, privacy-preserving computing across industries.
Mutahar Mujahid Mohammed, Hemasree Koganti, Abdul Hadi, Sai Krishna Akula · 6 authors
Traditional and digital voting systems both have their flaws, such as being vulnerable to fraud, having limited auditability, and being controlled by a central authority, which poses a growing threat to the honesty, openness, and safety of elections. This study seeks to solve the problem by exploring the potential of a voting system built on the blockchain that would guarantee voter anonymity, eliminate single points of failure, and offer end-to-end verifiability. A hybrid blockchain architecture is proposed, combining permissioned networks for high performance with public blockchain anchoring for transparency and fairness. A prototype implemented on Hyperledger Fabric was evaluated through simulated municipal elections with 10,000 virtual voters, achieving an average vote processing latency of 0.75 seconds, throughput of 4,000 votes per minute, 100% vote integrity, 99.97% system uptime, and full voter anonymity via zero-knowledge proofs. The results confirm that the proposed system can meet the performance, scalability, and privacy requirements for secure digital elections, while identifying key challenges—such as scalability, regulatory compliance, and digital inclusion—that must be addressed for real-world deployment.
Federated Learning (FL) offers a promising paradigm for privacy-preserving collaborative training, yet it remains highly vulnerable to adversarial behaviors, client unreliability, and challenges associated with non-independent and identically distributed (non-IID) data. Existing secure aggregation techniques, while preserving confidentiality, fail to guarantee the integrity and trustworthiness of model updates, leaving FL deployments exposed to poisoning and consistency attacks. This work introduces FL-SMPC++, a robust and privacy-preserving FL framework designed to address these challenges. The primary objective is to develop a scalable solution that ensures verifiable, privacy-preserving aggregation while mitigating malicious client behaviors, dropouts, and data heterogeneity. Our approach integrates Secure Multi-Party Computation (SMPC), Pedersen commitments, and zero-knowledge proofs (ZKPs) to cryptographically bind clients' submitted updates to their validation outcomes without revealing private data. We propose a dynamic client selection strategy based on shared validation performance, a dropout-tolerant threshold aggregation protocol, and a warm-up initialization phase to counteract non-IID distributions. Comprehensive experiments on MNIST, CIFAR-10, FEMNIST, and UCI Heart Disease show that FL-SMPC++ consistently outperforms FedAvg, FedProx, and FedNova. For example, under a label-flipping attack with 30% malicious clients on CIFAR-10 (non-IID), FL-SMPC++ achieves 78.9% accuracy compared to 67.4% for FedAvg, representing an absolute gain of 11.5%. Across datasets, the framework limits accuracy degradation to 6–8% under attack, while baselines suffer 13–20% losses. These results demonstrate that FL-SMPC++ achieves strong cryptographic privacy guarantees together with empirically validated resilience and convergence, offering a scalable and practical blueprint for trustworthy FL in adversarial and resource-constrained environments. • A novel FL framework combines SMPC, commitments, and zero-knowledge proofs. • Ensures submitted model updates match validated ones without revealing them. • Uses dynamic validation for secure and fair client selection. • Tolerates client dropouts using a threshold-based aggregation mechanism. • Outperforms baseline FL methods under adversarial and non-IID conditions.
Cloud computing facilitates scalable data sharing across multiple organizations and users, but also raises concerns about data privacy. Matchmaking encryption (ME) is a prominent technique that enforces bilateral access control in cloud services such as cloud marketplace, allowing both senders and receivers to specify policies for the encrypted data to be revealed. However, receivers may be at risk of being exposed to malicious or harmful content, thus undermining their trust in cloud service platforms. To this end, we introduce MBAC, a content-moderated bilateral access control framework for privacy-preserving cloud data sharing services, which allows receivers to acquire data from authentic senders while preserving their anonymity, and report malicious content in a verifiable manner, i.e., empowering the service provider to hold senders accountable. MBAC is built upon a novel primitive called franking broadcast ME (FBME), which generates a franking signature for the data by designating the service provider as the moderator to ensure accountability and deniability, and encrypts both the data and its franking signature while embedding the sender secret key for privacy and authenticity. We then present a concrete construction of FBME from key-private public key encryption, strongly unforgeable one time signature and non-interactive zero-knowledge proof. Formal security analysis and extensive experiments demonstrate that MBAC provides efficient bilateral access control and content moderation for cloud data sharing services.
Yang Xu, Qixin Wang, Yufei Ren, Ying Hu · 7 authors
ABSTRACT Transitive signatures are a special type of homomorphic signature proposed by Turing Award winners Micali and Rivest, which are highly suitable for authenticating dynamically growing graph‐based data systems. In such a signature scheme, anyone with the signer's public key is allowed to generate a signature for a composed edge , from two signatures on adjacent edges and . To prevent the problem of malicious dissemination of signatures by verifiers leading to data privacy leakage, researchers have proposed a series of universal designated verifier transitive signature (UDVTS) schemes. However, existing work requires that the designated verifier create its own secret‐public key pair using the public key parameters provided by the signer. Besides, these schemes suffer from significant performance defects due to expensive pairing or exponentiation operations. In this work, we design a pairing‐free and exponentiation‐free UDVTS proof scheme based on the SM2 digital signature algorithm and a zero‐knowledge proof scheme. We prove the security of our construction based on rigorous cryptographic assumptions. The performance comparison with related work shows that our UDVTS proof scheme has an optimal computational cost and desirable communication cost. For example, compared to the state‐of‐the‐art work, we reduce the signing cost by and the designated verification cost by .
In recent years, permissionless blockchains have emerged as the foundation for a wide range of decentralized applications, including cryptocurrency management, decentralized finance, auctions, voting systems, digital ownership, and more. The reliability of these applications depends on the security guarantees offered by the underlying blockchain protocols. Although prominent permissionless blockchains often base their security on formal analyses and claims, these assessments typically rely on simplifying assumptions, such as the existence of an honest majority, or overlook critical factors, like the influence of reward distribution on system security. Permissionless blockchains incentivize their participants, referred to as miners or validators, by rewarding them with cryptocurrency to encourage active participation in the protocol. The incentive mechanism plays a crucial role in maintaining the security of the blockchain by motivating participants to follow the desired, or honest, behavior. Since validators and miners are typically rational actors seeking to maximize profit, any flaw in the incentive design may motivate them to deviate from honest behavior in pursuit of an unfair advantage. Such exploitation of the incentive mechanism can lead to a destabilizing situation in which disadvantaged participants may choose either to deviate from honest behavior or to eventually abandon the network, with both possibilities threatening the blockchain's continued progress and stability. In this thesis, we analyze the incentive mechanisms behind several prominent permissionless blockchains, including Bitcoin, Cardano, and Ethereum. Our focus is primarily on potential fairness attacks that allow adversarial miners or validators to increase their profits by capturing an unfair share of blocks. As a first step, we analyze the incentive structure in Bitcoin, the pioneering Proof-of-Work (PoW) blockchain. Specifically, we examine the temporal aspects of profitability associated with one of the most well-known fairness attacks, namely selfish mining, which aims to exclude honest blocks from the longest chain. Building on this, we assess the role of Bitcoin's difficulty adjustment mechanism in mitigating such attacks. We then explore the implications of mining pool rationality for Bitcoin's security and propose novel attack vectors that could further increase an adversary's profit in a rational setting. Additionally, we investigate how upcoming Bitcoin reward halvings and the transition toward a transaction-fee era can impact Bitcoin's security by lowering the security threshold and increasing the potential for immediate profitability. As a next step, we analyze the incentive mechanisms of Proof-of-Stake (PoS) protocols. We examine longest-chain PoS protocols, such as Cardano, and show that due to the block proposer predictability inherent in PoS systems, the selfish mining attack can be even more destructive in this context. We also analyze Ethereum as a voting-based PoS protocol. These protocols are susceptible to block reorganization attacks, in which an adversarial validator attempts to exclude blocks with weak voting support from the canonical chain. We identify a vulnerability in Ethereum's reward mechanism that results from the centralized distribution of vote rewards. If exploited, this vulnerability can lead to both short- and long-range reorganization attacks. To address this issue, we propose a fairer and more decentralized reward mechanism for Ethereum. We hope the insights learned from this thesis will help identify potential incentive-based vulnerabilities in permissionless blockchains and contribute to the design of fairer blockchain protocols.
Zero Trust is now the de facto standard to secure cloud-native, distributed, and AI-driven enterprise infrastructures. It's not only crucial to address human identities but also to secure non-human entities such as APIs, software agents, RPA bots, and smart city workloads. As hybrid infrastructures become the new normal and agentic AI systems (e.g., self-driving cars) grow more autonomous, identity remains the most stable and trustworthy security control plane. This document proposes an intent-aware Zero Trust Identity Architecture designed to consolidate governance, authentication, and access control for human and non-human entities. The architecture consists of decentralized identity provisioning, policy-as-code enforcement, real-time telemetry ingestion, trust scoring, and AI-powered intent detection to provide inputs for continuous verification and least privilege enforcement. Compliant with standards such as NIST SP 800-207, NIST SP 800-63, CISA Zero Trust Maturity Model, and DoD's Zero Trust Strategy, the architecture also aligns with industry developments from Microsoft Entra ID, AWS IAM Identity Center, Google BeyondCorp, SPIFFE/SPIRE, and W3C DIDs. The whitepaper explores use cases in healthcare, finance, retail, and industrial IoT spaces that are struggling with unique challenges like OT/IT convergence, multi-user devices, and governance of sensitive data access. High-profile attacks such as SolarWinds, MOVEit, and Log4Shell are broken down to highlight weaknesses in legacy IAM architectures and underscore the need for intent-based security. By intersecting behavior, purpose, and identity, this architecture remakes trust in hybrid, edge, and cloud-native settings with a conclusion of actionable paths of mitigation and a vision for intent-based Zero Trust governance
This paper presents a novel Zero-Knowledge Machine Learning (zkML)-assisted framework for secure identity enrollment in Zero Trust Network (ZTN) architectures. The proposed system addresses the limitations of static credential-based authentication by integrating zkML-driven behavioral validation with permissioned blockchain-based token issuance. A Non-Fungible Token (NFT) is used to encapsulate a one-time enrollment token (OTT) encrypted with the public key of the requesting user. The zkML layer verifies behavioral features prior to token issuance, ensuring that only users with legitimate interaction patterns receive access credentials. A permissioned Ethereum blockchain handles NFT creation and ownership management, while the enrollment process is executed through OpenZiti APIs for secure overlay network participation. Experimental evaluation shows that the zkML-validated system achieves a 96.3% fake user block rate and $98.7 \%$ NFT precision, outperforming traditional methods by significantly reducing unauthorized access. Although the zkML approach introduces a modest increase in processing time, the enhanced accuracy and security justify the trade-off. This work demonstrates the potential of combining AI-driven inference and verifiable blockchain mechanisms to achieve scalable, privacypreserving, and behavior-aware enrollment in decentralized network environments.
Mohamed Abdessamed Rezazi, Mouhamed Amine Bouchiha, A. Bendada, Yacine Ghamri-Doudane
Roaming settlement in 5G and beyond networks demands secure, efficient, and trustworthy mechanisms for billing reconciliation between mobile operators. While blockchain promises decentralization and auditability, existing solutions suffer from critical limitations-namely, data privacy risks, assumptions of mutual trust, and scalability bottlenecks. To address these challenges, we present B5GRoam, a novel on-chain and zero-trust framework for secure, privacy-preserving, and scalable roaming settlements. B5GRoam introduces a cryptographically verifiable call detail record (CDR) submission protocol, enabling smart contracts to authenticate usage claims without exposing sensitive data. To preserve privacy, we integrate non-interactive zero-knowledge proofs (zkSNARKs) that allow on-chain verification of roaming activity without revealing user or network details. To meet the high-throughput demands of 5G environments, B5GRoam leverages Layer 2 zk-Rollups, significantly reducing gas costs while maintaining the security guarantees of Layer 1. Experimental results demonstrate a throughput of over 7,200 tx/s with strong privacy and substantial cost savings. By eliminating intermediaries and enhancing verifiability, B5GRoam offers a practical and secure foundation for decentralized roaming in future mobile networks.
G Anvith, Nithish Kushal Reddy, Ragini Tripathi, C. R. Kavitha
Federated learning enables multiple clients to collaboratively train a shared model without exchanging raw data, but it raises privacy and integrity concerns when model updates traverse untrusted channels. In this project, we develop a secure federated learning pipeline that combines the CKKS homomorphic-encryption scheme with Groth-16 zero-knowledge proofs to protect client updates during transmission and to verify that each update stays within an agreed-upon norm bound. We benchmark CKKS parameters (poly_modulus degree, coefficient_moduli, and scale) on real-world model vectors to identify an optimal setting—8192-degree with two primes (60-bit and 40-bit) at a 232scale—that offers sub-100 ms encryption, minimal error, and moderate ciphertext sizes ( 3.3 MB). Clients train a small convolutional network on disjoint partitions of the MSTAR SAR dataset, generate succinct ZK proofs for each 128-element weight chunk, encrypt those chunks under the selected CKKS context, and submit both ciphertexts and proofs to dedicated servers. The homomorphic-aggregation server sums encrypted updates, while the ZKP server enforces correctness by rejecting any proof that violates the norm constraint—demonstrated by catching an intentionally malicious client. End-to-end testing confirms that the combined CKKS+ZKP pipeline preserves model accuracy and ensures both confidentiality and integrity of federated updates.
To address the absence of process-level verifiability in federated learning, a verifiable architecture, zero-knowledge proof-verified and blockchain-audited federated learning (zk-BcFed), is proposed by integrating zero-knowledge proofs with blockchain. For each local model update, a multi-constraint zero-knowledge proof is generated by the client, and verified cryptographic evidence is recorded on-chain, enabling formal verification of local training without disclosure of private data. Across benchmark datasets including SVHN, FashionMNIST, and CIFAR10, among others, enabling zero-knowledge proofs is observed to produce a negligible change in accuracy while substantially improving robustness under model poisoning attacks. Collectively, zk-BcFed safeguards the computational integrity and correctness of federated learning and provides a reliable verifiability mechanism with modest overhead.
Wireless Sensor Networks (WSNs) are widely used in various applications that require secure and efficient data aggregation. A novel secure data aggregation scheme is proposed in this study. This is a novel combination of Blockchain technology and Zero-Knowledge Proofs (ZKPs). This scheme is broken down into three parts. The first is Blockchain based Data Aggregation for storage of tamper proof and immutable data. Second, anonymity with ZKP to provide privacy preservation and secure identity verification. Finally, we combined Blockchain and ZKP operation to achieve energy efficiency and robust security. The proposed scheme guarantees data confidentiality, integrity, anonymity, and resilience to malicious attacks, and at the same time handles the energy optimization problem in WSNs. We implemented the proposed scheme in MATLAB to evaluate its performance with metrics like stability, jitter, latency, and throughput. Simulation results show that the proposed scheme achieved robust security in terms of data integrity and confidentiality by slightly compromising on the availability aspect.
The article presents a comprehensive overview of zero-knowledge proof (ZKP) protocols as a fundamental concept of modern cryptography. The historical background of their emergence and the main properties ensuring reliability and confidentiality, i.e., completeness, soundness, and zero-knowledge — are considered. A classification of protocols into interactive and non-interactive ones is provided, with a special focus on modern solutions such as the zk-SNARK and the zk-STARK. The mathematical foundations of ZKPs are described in detail, including discrete logarithm proofs, the use of homomorphic encryption, polynomial commitments, hashing, and elliptic curves. Practical application areas are analyzed, including cryptocurrencies (Zcash, Ethereum), authentication systems, digital identity, and electronic voting. The advantages of using ZKPs are shown, such as enhanced privacy, reduced need for trusted intermediaries, and strengthened security. At the same time, key challenges are outlined, including scalability, implementation complexity, the problem of trusted setup, and potential vulnerability to quantum computing. It is concluded that zero-knowledge proof protocols are a powerful tool for ensuring confidentiality and reliability of digital systems, while further research is aimed at creating more efficient and quantum-resistant solutions.
Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.
The article presents a comprehensive overview of zero-knowledge proof (ZKP) protocols as a fundamental concept of modern cryptography. The historical background of their emergence and the main properties ensuring reliability and confidentiality, i.e., completeness, soundness, and zero-knowledge — are considered. A classification of protocols into interactive and non-interactive ones is provided, with a special focus on modern solutions such as the zk-SNARK and the zk-STARK. The mathematical foundations of ZKPs are described in detail, including discrete logarithm proofs, the use of homomorphic encryption, polynomial commitments, hashing, and elliptic curves. Practical application areas are analyzed, including cryptocurrencies (Zcash, Ethereum), authentication systems, digital identity, and electronic voting. The advantages of using ZKPs are shown, such as enhanced privacy, reduced need for trusted intermediaries, and strengthened security. At the same time, key challenges are outlined, including scalability, implementation complexity, the problem of trusted setup, and potential vulnerability to quantum computing. It is concluded that zero-knowledge proof protocols are a powerful tool for ensuring confidentiality and reliability of digital systems, while further research is aimed at creating more efficient and quantum-resistant solutions.
ChipmunkRing, a practical post-quantum ring signature construction tailored for blockchain environments. Building on our Chipmunk lattice-based cryptographic framework, this implementation delivers compact digital signatures ranging from 20.5 to 279.7KB, with rapid signing operations completing in 1.1-15.1ms and efficient validation processes requiring only 0.4-4.5ms for participant groups of 2-64 members. The cornerstone of our approach is Acorn Verification-a streamlined zero-knowledge protocol that supersedes the classical Fiat-Shamir methodology. This innovation enables linear O(n) authentication complexity using concise 96-byte cryptographic proofs per participant, yielding a remarkable 17.7x performance enhancement for 32-member rings when compared to conventional techniques. Our work includes rigorous mathematical security demonstrations confirming 112-bit post-quantum protection (NIST Level 1), extensive computational benchmarking, and comprehensive support for both standard anonymity sets and collaborative threshold constructions with flexible participation requirements.
D Abisha, Ashwanth Raj A, Sunil Kumar S, Senthil Kumar M J · 5 authors
The exponential growth of data driven applications in domains such as AI, healthcare, and IoT demands secure, transparent, and decentralized platforms for sharing high quality datasets. Traditional centralized systems suffer from opaque pricing, privacy risks, single points of failure, and lack of verifiable ownership, limiting trust and scalability. This paper proposes AquaStream, a decentralized data marketplace that leverages blockchain, the InterPlanetary File System (IPFS), and Ethereum smart contracts to tokenize datasets as non-fungible tokens (NFTs), ensuring verifiable ownership and tamper-proof traceability. The system employs dual-layer encryption (AES-256/RSA) to safeguard data during storage and transmission, while IPFS provides distributed storage to improve availability and reduce on-chain storage costs. Smart contracts based on ERC-20 and ERC-721 standards automate secure payments and access control, eliminating intermediaries and enhancing transparency. A React.js-based interface with MetaMask integration enables intuitive dataset management for both technical and non-technical users. Experimental evaluation on the Ethereum Sepolia testnet and Polygon Mumbai network demonstrates significant performance gains, including up to 73.6% reduction in gas costs using Layer 2 scaling and dataset retrieval latency as low as 4.2 seconds. These results confirm AquaStream’s efficiency, cost-effectiveness, and scalability. By bridging the gap between data providers and consumers, AquaStream fosters a collaborative ecosystem that promotes data ownership, privacy, and equitable monetization, offering a robust foundation for future decentralized data economies.