This paper presents a cryptographic ZeroKnowledge Proof (ZKP) protocol that allows the prover (P) to convince the verifier (V) that they know a secret number X, which is consistent with k residues in a Redundant Residue Number System (RRNS), without revealing the number X itself. The use of RRNS in this protocol provides enhanced efficiency and computational parallelism by splitting operations across independent moduli. This approach combines zero-knowledge properties with high performance, addressing the simultaneous need for security, privacy, and scalability - particularly in authentication and secure transactions.
Privacy-preserving reputation systems are critical for decentralized Web3 environments, where trust must be managed without centralized authorities. This paper presents a blockchain-based protocol leveraging Subjective Logic (SL) and Hybrid Homomorphic Encryption (HHE) to securely aggregate reputation scores while preserving user privacy. Subjective Logic enables modeling trust with quantified u ncertainty, a llowing for m ore fl exible tr ust enforcement across decentralized identity systems, marketplaces, and DAOs. To enhance performance and confidentiality, we integrate the PASTA symmetric cipher for efficient encryption of auxiliary data. Our protocol enables encrypted reputation aggregation, smart contract-based trust enforcement, and selective disclosure via zero-knowledge proofs. The proposed design balances efficiency, scalability, and privacy, making it well-suited for dynamic Web3 ecosystems requiring decentralized, privacy-preserving trust mechanisms.
M Savitha Devi, Ningthoujam Chidananda Singh, Thoudam Basanta Singh
Abstract - Blockchain enabled systems are more and more adopted in healthcare for secured processing of data, but current smart contract usage in healthcare leaks private patient data on execution. The contributions of this paper are two-fold: (1) it proposes a new framework that combines ZKPs with healthcare smart contracts/transactions to achieve full privacy preservation and (2) it discusses the security, usability, and the efficiency of the framework at the same time. Our proposed framework is based on zero-knowledge proof systems zkSNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) and zkSTARKs (Zero-Knowledge Scalable Transparent Argument of Knowledge) tailored for computer on medical data without revealing effectively. We conduct extensive analysis and prototype implementation to show that our framework is able to achieve perfect privacy preservation at a 1.87% computational overhead increase with respect to standard smart contracts. The system processes over 10,000 medical records with sub-second verification times and that meet the HIPAA requirements. Experimental results in diverse healthcare applications attest to the efficacy of the approach in practice, and show the substantial gain of privacy preservation (99.8% retention rate) and computational efficiency over the state-of-art algorithms. This paper bridges the gap between blockchain’s transparency and healthcare’s privacy requirements, laying the groundwork for secure and privacy-preserving blockchain based healthcare applications. Key Words: Zero-knowledge proofs, Smart contracts, Healthcare blockchain, Privacy preservation, zkSNARKs, zkSTARKs, Medical data security, HIPAA compliance
Online examinations that companies rely on more frequently have made traditional centralized Learning Management Systems (LMS) vulnerable to security threats and authentication problems and result manipulation issues. The place of storing examination data in a central location creates risks for intentional changes which compromises the process fairness as well as integrity. Currently deployed blockchain solutions are ineffective because they fail to deliver both economical solutions and scalable systems that work well with AI proctoring functions. This study introduces the BlockchainBased Examination Framework (BEF) as an integrated system which unites multi-LMS operation with blockchain-based safe storage along with AI-powered examination surveillance features for real-time academic dishonesty discovery. The system utilizes Ethereum together with Hyperledger Fabric and Solana blockchains to guarantee result security and activates Zero-Knowledge Proofs (ZKP) and ECDSA signatures for authentication privacy and implements AI models for live examination monitoring. A thorough examination analyzed speed and scalability and financial efficiency together to evaluate these aspects of the three platform frameworks. Solana demonstrates superior performance through its$\mathbf{6 5, 0 0 0}$Transactions Per Second along with its affordable transaction fee of $0.00025 that makes it the best scalable and efficient choice. The AI-proctoring system demonstrated a 97.8% accuracy level together with a$\mathbf{2. 2 \%}$false positive error rate which improved examination security. The research demonstrates blockchain implementation as a critical enhancement for exam security as well as transparency levels. The future project will concentrate on Ethereum Layer-2 scaling alongside deep learning improvements to AI proctoring systems for better cost reduction and flexibility.
M. Kamarunisha, T. Aarthi, A. Pavithra, J. Saira Banu · 6 authors
Blockchain is a secure, transparent digital ledger that reduces the need for a centralized authority to enable secure data transmission. Blockchain technology enhances security in healthcare by resolving key issues related to interoperability, privacy, and data security. Security hacks and third-party participation in medical data cause critical sharing issues with customized information on the internet, which results in security threats. To solve the issues, the method uses Lightweight Advanced Encryption Standard (LAES) method in this paper. To enhance the security, the four phases perform: block creation, key generation, data encryption & decryption, and key authentication. First, Proof of Secure Link (PoSL) is employed to create a secure and unalterable link between blocks, providing encrypted storage and transmission of medical records. Second, Random Key Generation (RKG) is used to create cryptographically secure random keys that secure sensitive health information while encrypting it. During the third stage, LAES is employed for data encryption and decryption in order to achieve robust security by utilizing symmetric encryption methods. Finally, Zero-Knowledge Proof (ZKP) is used for the verification of keys to validate secure verification of patient identity without disclosing personal information and, therefore, securing the patient's privacy while allowing access to legitimate users.
With the increasing need to train AI models on sensitive healthcare data, Federated Learning (FL) has emerged as a decentralized approach that avoids raw data sharing. However, existing methods such as DP-FL and zkFL still suffer from high privacy leakage, computational overhead, and scalability challenges. To overcome these limitations, this study introduces ZK-FedTransformer++, a novel privacy-preserving FL framework. It integrates lightweight TinyViT transformers, zk-SNARKs for verifiable training, differential privacy for statistical protection, and heuristic client selection for robust participation The approach provides secure model updates via cryptographic proof circuits and noise-perturbed gradients. Experiments based on the RSNA Breast Cancer Detection dataset achieve 91.2% accuracy and 35% less privacy leakage. Tools utilized include PyTorch, zk-SNARK libraries, and privacy accounting protocols. In summary, ZK- FedTransformer++ is an effective privacy enhancement, accuracy improvement, and scalability solution that is a feasible solution for secure, decentralized AI applications in real-world healthcare and IoT settings.
Michael Herbert Ziegler, Mariusz Nowostawski, Basel Katt
In this literature review, we critically examine the evolving landscape of privacy in blockchain systems, with a particular focus on the differentiation of privacy attacks and protective measures across three distinct layers: the on-chain layer; the off-chain layer; and on the infrastructure, i.e., peer-to-peer network layer. In this review, we categorize prevalent privacy attacks, such as transaction tracing, data leakage, and network surveillance, highlighting their implications at each layer. In addition, we evaluate a range of protective techniques, including cryptographic methods, zero-knowledge proofs, and other privacy-preserving protocols. We explore the compatibility of these privacy techniques with existing blockchain systems. By synthesizing current research and practical implementations, our aims are to provide a comprehensive understanding of privacy challenges and solutions in blockchain environments, identify gaps, and guide future developments in privacy-enhancing technologies within the blockchain ecosystem.
The rapid expansion of Internet of Things (IoT) deployments across smart environments introduces critical security challenges, particularly at the device identity and physical layers. Traditional cryptographic methods and Distributed Ledger Technologies (DLTs), while valuable, often fail to account for the constrained resources of IoT devices and their susceptibility to physical-layer attacks. This paper proposes a scalable, lightweight security framework that integrates Physical Unclonable Functions (PUFs), Zero-Knowledge Proofs (ZKPs), and a permissioned blockchain to establish end-to-end trust in distributed IoT ecosystems. PUFs act as hardware-rooted trust anchors, enabling secure key generation and unclonable device identity without relying on non-volatile memory. ZKPs facilitate mutual authentication by allowing devices to prove legitimacy without revealing any identifying information. A permissioned blockchain acts as a decentralised verification and audit layer, immutably recording authentication events and ensuring tamper resistance with controlled governance. The proposed architecture is designed to counteract physical tampering, spoofing, and identity forgery while remaining computationally viable for resource-constrained IoT devices. This work presents the foundation for a robust, privacy-preserving, and decentralised security model, bridging the gap between hardware-level assurance and scalable trust in future IoT deployments.
Physical Unclonable Functions (PUFs) and Hardware Security
T. Ratha Jeyalakshmi, Alamma Bh, H S Harshitha, K. Agarwal R.
Privacy of users and security of data are important issues that will be exposed to use in the Metaverse by use of Digital Twins (DTs). The current paper suggests a privacy-preserving system, which combines Zero-Knowledge Proofs (ZKPs) of secure identity verification and Federated Learning (FL) of decentralized model training. The framework allows for alleviating the risk of storing data in central facilities and preventing unauthorized access by locally processing data and using cryptographic solutions. The results produced by the evaluation prove that the proposed system is capable of attaining the necessary level of privacy of its users and ensuring reliable and scalable communications within the Metaverse applications.
Statistical witness indistinguishability is a relaxation of statistical zero-knowledge which guarantees that the transcript of an interactive proof reveals no information about which valid witness the prover used to generate it. In this paper we define and initiate the study of QSWI, the class of problems with quantum statistically witness indistinguishable proofs. Using inherently quantum techniques from Kobayashi (TCC 2008), we prove that any problem with an honest-verifier quantum statistically witness indistinguishable proof has a 3-message public-coin malicious-verifier quantum statistically witness indistinguishable proof. There is no known analogue of this result for classical statistical witness indistinguishability. As a corollary, our result implies SWI is contained in QSWI. Additionally, we extend the work of Bitansky et al. (STOC 2023) to show that quantum batch proofs imply quantum statistically witness indistinguishable proofs with inverse-polynomial witness indistinguishability error.
Timed signatures are cryptographic primitives that enable senders to predefine the validity period of a signature. Currently, two primary types of timed signatures have been developed. The first type, known as Verifiable Timed Signatures (CCS'2020), implements a delay before a signature becomes effective. The second type is Short-Lived Signatures (ASIACRYPT'2022), which allows for the setting of an expiration time for signatures upon creation. However, certain applications requiring time-sensitive authorization demand both activation and expiration times to be set, a requirement not fulfilled by the existing timed signature schemes. To overcome this limitation, we propose a novel flexible timed signature scheme called Time Interval Signatures (TIS). TIS combines Verifiable Delay Functions and Short-Lived Signatures with our Zero-Knowledge Proof of Product, facilitating the flexible setting of both activation and expiration times for the signature. Building on TIS, we present TimeGuardian, a time-bound NFT rights protocol that enables presetting authorization and revocation periods for NFT usage rights. Experimental results show that TIS achieves signature size reductions of 98.67% and 57.14% compared to existing verifiable timed signature solutions.
Designing secure electronic voting systems that truly protect voter privacy, ensure vote accuracy, and allow independent verification continues to pose serious difficulties. Many current cryptographic approaches require excessive computational resources and use encryption keys that are too large for practical implementation. This paper proposes modifications to the Chaum, Pedersen and Cramer, Franklin, Schoenmakers, and Yung voting protocols by integrating elliptic curve cryptography (ECC), which offers stronger security per bit and more compact key representations. The use of ECC allows for reduced parameter sizes while maintaining resistance against known attacks, including those targeting the discrete logarithm problem. We present detailed adaptations of these protocols on elliptic curves and demonstrate how they preserve core security properties such as vote secrecy, universal verifiability, and resistance to double voting under a more efficient cryptographic framework. Our findings contribute to the development of scalable, high-assurance e-voting mechanisms suitable for modern digital infrastructures. The presented modifications significantly enhance the scalability and efficiency of e-voting systems without compromising cryptographic strength.
Anne S. Tsui, Farzam Boroomand, Arjen van Witteloostuijn, Wilfred Mijnhardt
This paper maps how management scholarship has taken up the United Nations' Sustainable Development Goals (SDGs) across the past two decades with a particular focus on how Management and Organization Review (MOR) compares to 18 flagship journals in accounting finance management marketing and operations. Building on a 55⁃year 18⁃journal dataset the authors zero in on 2005—2024—the decade before and after the SDGs' 2015 launch—and add MOR as a 19th journal to assess whether Chinese management research has been especially receptive to SDG⁃oriented work. Methodologically the team uses an ensemble of three AI systems—a keyword / semantic model from Rotterdam School of Management OpenAI GPT⁃4. 1 and Claude Sonnet 3. 7—to score each article abstract against all 17 SDGs. Articles are tagged to a goal when at least two models concur (“majority rule”) allowing multi⁃label assignment. Inter⁃model agreement is high (most pairwise correlations > 0. 90) and the resulting SDG ratio—the share of a journal's output mapped to at least one SDG—serves as a transparent scalable indicator of a journal's social⁃value orientation. Across the 20⁃year window SDG engagement rises markedly after 2015. In the 18 journals the SDG ratio climbs from a pre⁃2015 baseline of 9% to 31% in 2015—2024. MOR exhibits both higher levels and stronger growth 28. 4% of its 2005—2014 papers are SDG⁃linked jumping to 43. 3% post⁃2015—about 14 percentage points above the contemporaneous 18⁃journal average. Aggregated over 2005—2024 36. 7% of MOR's 365 articles map to at least one SDG compared with 26. 4% of the 24,508 articles in the comparison set indicating a consistently stronger SDG orientation at MOR. Topic coverage is uneven but broadly aligned across journals. Four goals dominate in both MOR and the 18 journals SDG08 (Decent Work and Economic Growth) SDG09 (Industry Innovation & Infrastructure) SDG10 (Reduced Inequality) and SDG16 (Peace Justice & Strong Institutions). MOR also shows attention on SDG12 (Responsible Consumption & Production) clearing the 1% threshold there whereas the 18⁃journal group surpasses MOR on SDG03 (Good Health & Well⁃Being) and SDG05 (Gender Equality). Several ecology⁃focused goals (e. g. SDG 13—15) remain comparatively underrepresented overall underscoring opportunities to bind environmental stewardship more tightly to mainstream management theories of strategy organizing and innovation. The findings illuminate the agenda⁃setting power of editorial policy. MOR's mission—to advance theory from and about China while cultivating humanistic stakeholder⁃oriented inquiry—appears to institutionalize stronger incentives for socially consequential work through topic selection special issues and review criteria. This suggests that journals can accelerate the field's pivot toward responsible research without sacrificing rigor echoing the Responsible Research in Business and Management (RRBM) movement's dual mandate of credibility and usefulness. The paper also positions the SDG ratio as a complementary metric to citations—one that foregrounds societal relevance. While an SDG tag is not proof of real⁃world impact systematic SDG mapping offers a common language for scholars editors and funders to monitor progress identify blind spots (notably climate and biodiversity) and align resources and evaluations with global development priorities. Methodologically the study endorses AI ensemble triangulation as a reliable scalable approach for large⁃corpus content analysis with the caveat that multi⁃model checks and transparency are essential. In sum management research has shifted—unevenly but decisively—toward societal stewardship since 2015. MOR stands out as a field leader demonstrating how editorial stewardship can galvanize SDG⁃relevant scholarship. The road ahead is clear deepen coverage of neglected ecological and equity goals maintain methodological pluralism and use SDG⁃aligned incentives to translate rigorous scholarship into knowledge that advances the common good.
Machine learning as a service (MLaaS) has emerged as a prominent computing paradigm where users send sensitive data to cloud servers that subsequently return computed results. In MLaaS, ensuring the correctness of these results poses a significant challenge. While zero-knowledge proof (ZKP) presents a potential solution, they often come with substantial memory overhead. Moreover, there is insufficient attention given to the privacy risks associated with untrustworthy servers, which could jeopardize users' sensitive information. In this paper, we introduce$\text{Vp}^{3}\text{CNN}$, a three-party verifiable privacy-preserving convolutional neural network (CNN) inference scheme. In$\text{Vp}^{3}\text{CNN}$, users verify the correctness of CNN inference through a lightweight ZKP protocol grounded in vector oblivious linear evaluation. This protocol is designed to ensure that servers incur minimal memory overhead while maintaining the integrity of the verification process. Based on the optimization of the convolutional relation, the scheme reduces the computational cost associated with the verification process of the convolution operations. In addition,$\text{Vp}^{3}\text{CNN}$employs two non-colluded servers to protect user data privacy via secret sharing schemes. We implement our scheme in C++ and evaluate its performance using the MNIST and CIFAR-10 datasets. Experimental results demonstrate that, compared to existing methods,$\text{Vp}^{3}\text{CNN}$achieves a speedup of 4–5 times in convolution verification while maintaining nearly consistent communication overhead. Importantly,$\text{Vp}^{3}\text{CNN}$does not compromise the accuracy of CNN inference, achieving an accuracy of 97.8% on the MNIST dataset.
Prizadevanje za vzpostavitev evropskega okvira za digitalno identiteto je leta 2024 doprineslo do pomembnega koraka naprej, saj je 20. maja 2024 začela veljati novela EU uredbe št. 910/2014 za e-identifikacijo in storitve zaupanja, ki jo poznamo tudi kot Uredba eIDAS 2.0. Ta vzpostavlja pravno podlago za uvedbo evropske denarnice za digitalno identiteto po vsej EU. Z denarnico bodo uporabniki lahko tudi varno pridobili, shranili in delili svoje pomembne dokumente, npr. o izobrazbi in licencah, pooblastila za zastopanje pravnih oseb, finančne podatke in podatke o družbah, ter elektronsko podpisovali oz. v primeru denarnic za podjetja elektronsko žigosali dokumente. Da bi dosegli interoperabilnost med denarnicami, izdanimi s strani držav članic, so v izvedbenih aktih k Uredbi eIDAS 2.0 določeni standardi za evropsko denarnico, ki jih morajo upoštevati vse implementacije denarnic po državah, pravila za certificiranje denarnic in sporočanje Evropski komisiji. Skupne zahteve za denarnico se pripravljajo v okviru Arhitekturnega in referenčnega okvirja (ARF), poleg tega pa Evropska komisija pripravlja tudi referenčno implementacijo denarnice. V prispevku so podrobneje predstavljene nekatere visokonivojske zahteve ARF, ki se nanašajo na področje zasebnosti, še posebej uporaba metod ničelno spoznalnih dokazov (angl. Zero knowledge Proof) za zagotavljanje zasebnosti v ekosistemu denarnic.
Elections and referendums play a vital role in a democratic society, which enable individuals to make collective decisions. In the Internet information era, electronic voting has replaced traditional paper voting. However, the centralized architecture of the electronic voting system is vulnerable to attacks and the voting records can be easily changed or even deleted. Blockchain, as a decentralized and trustworthy distributed network, offers new means for electronic voting systems. Current blockchain- based voting systems still face several challenges: they cannot achieve full verifiability in self-tallying, cannot tolerate invalid or abstained ballots, and cannot prevent Sybil attacks either. To address these challenges, we use some cryptographic primitives to construct a blockchain- based decentralized self-tallying verifiable referendum scheme to provide a transparent and secure remote electronic voting system. First, we use range zero-knowledge proofs to verify the ballot content, and for the first time, propose a novel method using bilinear pairing to verify decryption results, which significantly reduces computational burden and gas consumption during verification. Second, we ingeniously combine a threshold decryption system with a blockchain-based deposit mechanism: invalid or abstained ballots are excluded from the tally, and voters casting such ballots are incentivized to publish their partial private keys through the deposit mechanism, ensuring their exit from the decryption process without disrupting the election. We also establish an innovative access mechanism for smart contract that effectively prevents Sybil attacks. Theoretical analysis and experimental results demonstrate that our system is secure, feasible, and efficient.
The merging of Artificial Intelligence (AI) with the Internet of Things (IoT) has sparked a swift transformation in AIoT systems, allowing for real-time intelligence in smart cities, industries, and homes. Yet, these advancements bring about increasing worries regarding data privacy, device trust, and potential security threats-particularly with the emergence of quantum computing. This paper introduces a secure and privacy focused AIoT framework that integrates Federated Learning with Differential Privacy, Zero-Knowledge Proofs (ZKP) for device authentication, and Post-Quantum Cryptography(CRYSTALSKyber) to protect model updates on the blockchain. Unlike conventional methods that depend on cloud processing and expose sensitive data, this innovative system allows for on-device model training through TinyML, ensuring that data remains on the device. A practical implementation using ESP32-S3 devices in both a smart classroom and home environment showcases the framework's effectiveness. The results indicate a 12% boost in privacy, a 35% reduction in communication costs, and an 8.7% increase in model accuracy compared to traditional methods. This architecture tackles significant unresolved challenges in AIoT by securing data at the edge, preventing device spoofing, and preparing for future quantum threats-making it an excellent choice for privacy-sensitive, real-time AIoT applications.
Blockchain has evolved from cryptocurrency infrastructure to a foundation for decentralized finance, supply chain, and digital identity. However, widespread adoption faces three main barriers that are high energy use from traditional consensus, fragmented networks, and static, rule-based smart contracts. This work presents EcoChainX, a modular framework integrating AI-driven automation, sustainable consensus, robust interoperability, and advanced privacy features. Its four-layer architecture consists of AI modules for anomaly detection and smart contract optimization, energy-efficient consensus protocols, cross-chain interoperability, and privacy-preserving technologies such as zero-knowledge proofs and quantum-resistant cryptography. Through theoretical modeling, prototyping, and empirical testing, EcoChainX addresses scalability, sustainability, security, and privacy. By addressing them, this framework paves the way for responsible blockchain ecosystems capable of supporting the next generation of decentralized applications. Empirical results demonstrate that EcoChainX achieves a 97% reduction in energy consumption compared to traditional Proof-of-Work systems, increases transaction throughput by over 20 times (exceeding 10,000 TPS), reduces smart contract vulnerabilities by 60 % through AI-driven anomaly detection, and enables cross-chain transactions with a latency reduction of 80 %, establishing a new benchmark for sustainable and interoperable blockchain infrastructures.
Elliptic curve-based zero-knowledge proof (ZKP) protocols typically use multi-scalar multiplication (MSM) as a key primitive, making it one of the major performance bottlenecks in real-world ZK provers. In this paper, we present an FPGA-based MSM accelerator that achieved state-of-the-art performance in the 2023 ZPrize, a competition dedicated to advancing zero-knowledge cryptography, with submissions from both academia and industry. Our design achieves this through two primary innovations. First, we adopt affine (two-coordinate) representations for elliptic curve points, rather than resorting to projective coordinates, and leverage a batched inversion strategy to handle the expensive multiplicative inverse operation. Although many implementations extend points to projective form to avoid explicit inversions, they incur additional multiplications. By retaining affine coordinates and using the Montgomery trick (where multiple denominators are inverted at once), our accelerator reduces the overall number of real inversions per batch of point additions, drastically improving throughput while preserving a simpler coordinate system. Second, we introduce a novel hazard avoidance scheme that eliminates pipeline stalls arising from our high-latency elliptic curve addition pipeline. Through early detection and reordering of hazards, the pipeline remains fully utilized, thus maintaining continuous high throughput.
Abstract Oblivious transfer is a type of message transfer in which a sender transmits one out of many potential pieces of information to the receiver, but she has no knowledge about the actual piece of information being received by the receiver. Oblivious transfer is a deceptively simple scheme that has many possible applications such as secure multiparty computation, private set intersection, federated learning, zero-knowledge proofs, accessing sensitive data etc. Security of most classical oblivious transfer protocols is based upon the unproven assumptions about the computational complexity of certain number theoretic problems such as integer factorization. So, existing classical protocols for oblivious transfer are only computationally secure and not unconditionally secure. Although many quantum oblivious protocols have been proposed lately, they are not simple and easy to implement. In the present work we propose a quantum oblivious transfer protocol that is efficient, simple and easily implementable with the existing quantum technology.
Bruno M. F. Ricardo, Lucas C. Cardoso, Leonardo T. Kimura, Marcos A. Simplício · 5 authors
In 2023, Barreto and Zanon proposed a three-round Schnorr-like blind signature scheme, leveraging zero-knowledge proofs to produce one-time signatures as an intermediate step of the protocol. The resulting scheme, called BZ, is proven secure in the discrete-logarithm setting under the one-more discrete logarithm assumption with (allegedly) resistance to the Random inhomogeneities in a Overdetermined Solvable system of linear equations modulo a prime number p attack, commonly referred to as ROS attack. The authors argue that the scheme is resistant against a ROS-based attack by building an adversary whose success depends on extracting the discrete logarithm of the intermediate signing key. In this paper, however, we describe a distinct ROS attack on the BZ scheme, in which a probabilistic polynomial-time attacker can bypass the zero-knowledge proof step to break the one-more unforgeability of the scheme. We also built a BZ variant that, by using one secure hash function instead of two, can prevent this particular attack. Unfortunately, though, we show yet another ROS attack that leverages the BZ scheme’s structure to break the one-more unforgeability principle again, thus revealing that this variant is also vulnerable. These results indicate that, like other Schnorr-based strategies, it is hard to build a secure blind signature scheme using BZ’s underlying structure.
Emerging applications in cloud computing, big data, and the Internet of things have driven the advancement and implementation of security protocols, including secure multi-party computation, fully homomorphic encryption, and zero-knowledge proofs, to meet heightened security demands. Designing cryptographic permutations and block ciphers using a partial substitution-permutation network (P-SPN) approach, where the nonlinear part does not cover the entire state, has recently gained attention due to favorable implementation characteristics in various scenarios. For the word-oriented P-SPN schemes with a fixed linear layer, the choice of the maximum distance separable (MDS) matrix significantly affects the security level provided by P-SPN designs. If the MDS matrix is chosen weak, it will allow for extremely maximum invariant subspace that pass the entire rounds without activating any non-linear operation. Firstly, we investigate the properties of a special block matrix with circulant block, specifically utilized within the linear layer matrix of P-SPN structure schemes. Subsequently, our investigation extends to present the annihilating polynomial of low degree for these specific type of matrices, as well as to put forward the range of determining their minimal polynomial degree. Finally, this study articulates a lower bound estimated for the dimension of the maximum invariant subspace within the P-SPN structure schemes when integrated with the aforementioned matrix type. In scenarios where the S-box number$s$= 1 in the P-SPN structure schemes, we achieve a precise determination of the dimension of maximum invariant subspace. Conversely, for cases with$s$> 1, with some certain specific conditions, our research establishes more compact lower bound for the dimension of the maximum invariant subspace. The research results of this paper offer valuable design guidance for the development of matrices within the linear layer of P-SPN architecture schemes.
Jingcheng Zhang, Yekai Zhou, Yingxuan Ren, Man Ho Au · 9 authors
Advancements in sequencing technologies grant individuals unprecedented access to their genomic data. However, existing data management systems or protocols are inadequate in privacy protection, limiting individuals' control over their genomic information, hindering data sharing, and posing challenges for biomedical research. Therefore, demand exists for an owner-governed system fulfilling owner authority, life cycle data encryption, and verifiability simultaneously. Here, we realized Governome, an owner-governed data management system empowering individuals with real-time control over their genomic data. Governome leverages a blockchain to manage transactions and permissions, granting data owners dynamic permission management with full transparency on data usage. It uses homomorphic encryption and zero-knowledge proofs to enable genomic data storage and computation in an encrypted and verifiable form throughout its life cycle. Governome can support versatile genomic applications. We implemented and tested individual variant query, cohort study, genome-wide association study (GWAS) analysis, and forensics on 2,504 1000 Genomes Project (1kGP) genomes, demonstrating its robustness and scalability. Governome is open-source at https://github.com/HKU-BAL/Governome.