The purpose of Internet of Things (IoT) security is to ensure the availability, confidentiality, and integrity of IoT networks. However, due to the heterogeneity of IoT devices and the possibility of attacks of various kinds from both inside and outside the network, securing an IoT network is a difficult task. Handshake protocols are useful for achieving mutual authentication, which allows secure inclusion of devices into the network. By verifying that the information they receive is accurate and from a trusted source, mutual authentication minimizes the possibility that a malicious actor will compromise their connections. However, handshake protocols do not protect devices from attackers in the network. Use of autonomous anomaly detection and blacklisting prevents nodes with anomalous behavior from joining, re-joining, or remaining in the network. Similarly, trust scoring is another popular method that can be used to increase the resilience of the network against trust based system attacks. In view of the above, the contributions of this paper are three-fold. First, to ensure the security of the IoT network from outsider attacks in a zero-trust environment, we propose a new handshake protocol based on Physical Unclonable Functions that can be used in IoT device discovery and mutual authentication between the IoT device and the server. The proposed protocol is resilient to Man-in-the-Middle, replay and forgery attacks, as proven in our security analysis. Secondly, we propose a real-time intrusion and anomaly detection framework based on machine learning to prevent network-based attacks from insiders. Finally, we propose a trust system which utilizes feedback mechanisms based on smart contracts for managing the trust of a dynamic IoT network to increase resilience against behavioral attacks. Simulation results show that by using blacklisting, our trust management model provides greater resilience against trust-based attacks compared to similar blockchain-based trust models in the literature, and the proposed distributed IoT network security framework can secure an IoT network from both internal and external attacks, even in an environment where half of the devices in the network are compromised.
Gayatri M Bhandari, Nitin M Shivale, Shrishail S Patil, Pranav Prajapati · 7 authors
Federated learning is an emerging technology that can revolutionize the training of machine learning models. Federated learning refers to an approach to training a machine learning model in a decentralized and collaborative fashion. A central server distributes the model to client devices, where it is trained locally using the clients’ own data. The client then sends the updated model weights to the server, which aggregates them to update the global model. This paper introduces a federated learning platform designed to enable collaborative training of machine learning models across multiple client devices while preserving data privacy. The platform supports a range of supervised learning algorithms, including convolutional neural networks and decision trees, and is compatible with widely used frameworks such as TensorFlow, PyTorch, and Flower. It offers a user-friendly interface where model developers can upload or deploy their machine learning models to a central server. Clients can then access these models and train them locally using their own data. The platform's modular design ensures flexibility in deployment and efficiency in handling real-world applications. The key features of this application include a model repository, secure API access for client integration, local model training capabilities on user-end devices, and a user-friendly UI. The platform aims to democratize machine learning by enabling distributed model training and deployment, promoting collaboration and efficiency across diverse use cases. The scalable infrastructure supports real-time inference, on-device training, and secure data handling, making it ideal for industries ranging from healthcare to finance and beyond.
Ruslan Kysil, István András Seres, Péter Kutas, Nándor Kelecsényi
This work explores the application and efficient deployment of (standardized) post-quantum (PQ) digital signature algorithms in the blockchain environment. Specifically, we implement and evaluate four PQ signatures in the Ethereum Virtual Machine: W-OTS+ , XMSS, SPHINCS+, and MAYO. We focus on optimizing the gas costs of the verification algorithms as that is the signature schemes’ only algorithm executed on-chain, thus incurring financial costs (transaction fees) for the users. Hence, the verification algorithm is the signature schemes’ main bottleneck for decentralized applications. We examine two methods to verify post-quantum digital signatures on-chain. Our practical performance evaluation shows that full on-chain verification is often prohibitively costly. Naysayer proofs (FC’24) allow a novel optimistic verification mode. We observe that the Naysayer verification mode is generally the cheapest, at the cost of additional trust assumptions. We release our implementation called poqeth as an open-source library.
Democratic institutions increasingly rely on verifiable digital trust to enable fair participation and evidence-based decisions. Truvry is a decentralized protocol that converts behaviour-based evidence (usage patterns, transaction integrity, peer attestations) into portable cryptographic proofs that remain independent of any single platform or identifier, allowing individuals to transfer trust capital across domains while preserving privacy. The current prototype is zero-knowledge–compatible; in this version we use hashed proof anchoring and field-level redaction (no zk-SNARK module is deployed), with configurable smart-contract verifiers. By decoupling trust from identity, Truvry widens citizen inclusion, mitigates gatekeeping bias, and supplies auditable inputs for AI-mediated governance. In prototype tests (n=112), end-to-end proof issuance averaged 3.7 s (fastest local 1.4 s), verifier parse+check averaged 1.8 s, and the current minimum anonymization entropy is 8.9 bits; gas costs for optional on-chain anchoring remained below US$0.02. All results are based on simulated user streams; a production pilot is planned.
Dan Ivanov, Tristan Freiberg, Shahabi, Shirin, Jonathan Gold · 5 authors
DSperse is a modular framework for distributed machine learning inference with strategic cryptographic verification. Operating within the emerging paradigm of distributed zero-knowledge machine learning, DSperse avoids the high cost and rigidity of full-model circuitization by enabling targeted verification of strategically chosen subcomputations. These verifiable segments, or "slices", may cover part or all of the inference pipeline, with global consistency enforced through audit, replication, or economic incentives. This architecture supports a pragmatic form of trust minimization, localizing zero-knowledge proofs to the components where they provide the greatest value. We evaluate DSperse using multiple proving systems and report empirical results on memory usage, runtime, and circuit behavior under sliced and unsliced configurations. By allowing proof boundaries to align flexibly with the model's logical structure, DSperse supports scalable, targeted verification strategies suited to diverse deployment needs.
Open access
2 source records
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
This work presents a cryptographic protocol for secure multi-party verification that achieves com putational privacy while maintaining exceptional computational efficiency. The proposed Position Based Commitment Protocol (PBCP) introduces a position-dependent nonce mechanism combined with cyclic verification architecture, enabling se cure computation over private inputs without re vealing individual parameters. Unlike existing commitment schemes that require complex cryp tographic assumptions, computationally expensive zero-knowledge proofs, or extensive public key in frastructure, Fundamental innovation lies in adapt ing physical laws of fluid dynamics to create nat ural mathematical relationships where each verifi cation equation contains multiple unknowns, mak ing parameter extraction computationally infeasible while preserving verification integrity. The proto col preliminary analysis suggests O(n) communica tion complexity with O(n2) verification complexity, providing substantial improvements over traditional Byzantine Agreement protocols that require O(n3) message exchanges. Comprehensive security analysis reveals robust resistance against statistical attacks with complexity O(R3) where R represents the pa rameter range, complete immunity to timing attacks through blind submission mechanisms, and resilience against collusion attacks involving up to n/2 − 1 ad versarial parties. The protocol’s unique cyclic neigh bor verification creates an interdependent validation network that prevents individual parameter extrac tion while maintaining system-wide integrity through mathematical interdependence rather than crypto graphic assumptions.
Proof-of-Work cryptocurrencies employ miners to sustain the system through algorithmic reward adjustments. We develop a stochastic model of the multicurrency mining market and identify conditions for stable transaction speeds. Bitcoin's algorithm requires hash supply elasticity $\le$ 1 for stability, while ASERT remains stable for any elasticity and can be interpreted as a form of stochastic gradient descent algorithm under a certain loss function. Interactions with other currencies can relax Bitcoin's stability requirements. Using a ``halving'' event, we estimate miners' hash supply elasticity and conduct counterfactual simulations. Our findings reveal Bitcoin's heavy reliance on low hash-supply elasticity and interactions with smaller cryptocurrencies, suggesting an algorithm upgrade is crucial for stability.
Evgeniya Ishchukova, Sergei Petrenko, A. Petrenko, Konstantin Gnidko · 5 authors
Today, blockchain technologies are a separate, rapidly developing area. With rapid development, they open up a number of scientific problems. One of these problems is the problem of reliability, which is primarily associated with the use of cryptographic primitives. The threat of the emergence of quantum computers is now widely discussed, in connection with which the direction of post-quantum cryptography is actively developing. Nevertheless, the most popular blockchain platforms (such as Bitcoin and Ethereum) use asymmetric cryptography based on elliptic curves. Here, cryptographic primitives for blockchain systems are divided into four groups according to their functionality: keyless, single-key, dual-key, and hybrid. The main attention in the work is paid to the most significant cryptographic primitives for blockchain systems: keyless and single-key. This manuscript discusses possible scenarios in which, during practical implementation, the mathematical foundations embedded in the algorithms for generating a digital signature and encrypting data using algorithms based on elliptic curves are violated. In this case, vulnerabilities arise that can lead to the compromise of a private key or a substitution of a digital signature. We consider cases of vulnerabilities in a blockchain system due to incorrect use of a cryptographic primitive, describe the problem, formulate the problem statement, and assess its complexity for each case. For each case, strict calculations of the maximum computational costs are given when the conditions of the case under consideration are met. Among other things, we present a new version of the encryption algorithm for data stored in blockchain systems or transmitted between blockchain systems using elliptic curves. This algorithm is not the main blockchain algorithm and is not included in the core of modern blockchain systems. This algorithm allows the use of the same keys that system users have in order to store sensitive user data in an open blockchain database in encrypted form. At the same time, possible vulnerabilities that may arise from incorrect implementation of this algorithm are considered. The scenarios formulated in the article can be used to test the reliability of both newly created blockchain platforms and to study long-existing ones.
The increasing reliance on cloud services demands advanced security mechanisms to protect sensitive data and ensure robust access control. This study addresses critical challenges in cloud security by proposing a novel framework that integrates blockchain-based smart contracts to enhance authorization and authentication processes. Smart contracts, as self-executing agreements embedded with predefined rules, enable decentralized, transparent, and tamper-proof mechanisms for managing access control in cloud environments. The proposed system mitigates prevalent threats such as unauthorized access, data breaches, and identity theft through an immutable and auditable security framework. A prototype system, developed using Ethereum blockchain and Solidity programming, demonstrates the feasibility and effectiveness of the approach. Rigorous evaluations reveal significant improvements in key metrics: security, with a 0% success rate for unauthorized access attempts; scalability, maintaining low response times for up to 100 concurrent users; and usability, with an average user satisfaction rating of 4.4 out of 5. These findings establish the efficacy of smart contract-based solutions in addressing critical vulnerabilities in cloud services while maintaining operational efficiency. The study underscores the transformative potential of blockchain and smart contracts in revolutionizing cloud security practices. Future research will focus on optimizing the system’s scalability for higher user loads and integrating advanced features such as adaptive authentication and anomaly detection for enhanced resilience across diverse cloud platforms.
This research explores the distributed database security storage and access control scheme based on IPFS and blockchain for the privacy issues such as sensitive data leakage and account security under the rapid development of Internet technology. The research background focuses on the contradictory status quo of data value enhancement and black-market data trading in the fields of intelligent medical care and unmanned driving, etc. Although the existing database security technology has made progress in encryption algorithms, dynamic protection, etc., it is still faced with the challenges of performance bottleneck and fine-grained access control of centralized architecture. The research aims to integrate the advantages of IPFS distributed storage and the tamper-proof characteristics of blockchain to construct a new type of secure storage system. Through theoretical analysis of IPFS peer-to-peer file system architecture, blockchain six-layer model (data layer, network layer, consensus layer, etc.) and AES/SM4 encryption algorithms, a system solution integrating blockchain smart contract and IPFS storage is designed: SM4 encrypts the original data and then stores it in IPFS, and achieves traceability through the blockchain record hash, and introduces the proxy re-encryption based on the identity technology to Realize dynamic access control. Experiments comparing the performance of MongoDB and IPFS show that in 5000 transactions, the delay of IPFS mode 12 nodes is reduced by 1.71 times compared with 6 nodes, which is significantly better than that of MongoDB's by 1.22 times; in the throughput test, IPFS increases linearly with the increase of nodes, while MongoDB decreases after the peak value. The study confirms that the combination of IPFS and blockchain can effectively reduce transaction latency by 31%, improve throughput by 30%, and safeguard the security of the whole data lifecycle through cryptographic technology. The results provide a decentralized security framework for distributed databases, with both theoretical innovation and engineering application value, which is of great practical significance for highly sensitive data fields such as healthcare and finance.
Watermarking protocols represent a possible solution to the problem of digital copyright protection of content distributed on the Internet. Their implementations, however, continue to be a complex problem due to the difficulties researchers encounter in proposing secure, easy-to-use and, at the same time, “trusted third parties” (TTPs)-free solutions. In this regard, implementations based on blockchain and smart contracts are among the most advanced and promising, even if they are affected by problems regarding the performance and privacy of the information exchanged and processed by smart contracts and managed by blockchains. This paper presents a watermarking protocol implemented by smart contracts and blockchain. The protocol uses a “layer-2” blockchain execution model and performs the computation in “trusted execution environments” (TEEs). Therefore, its implementation can guarantee efficient and confidential execution without compromising ease of use or resorting to TTPs. The protocol and its implementation can, thus, be considered a valid answer to the “trilemma” that afflicts the use of blockchains, managing to guarantee decentralization, security, and scalability.
Open access
Advanced Steganography and Watermarking Techniques
Gennaro Avitabile, Vincenzo Botta, Daniele Friolo, Ivan Visconti
Balancing immutability and compliance with regulations stands as a significant challenge in the realm of blockchain technology applications. Due to the increase of data-protection requirements (e.g., the GDPR in the EU), it is essential to address the problem of deleting data from a blockchain without compromising the security and transparency of the blockchain itself. Several works proposed techniques to address the data redaction problem. In their seminal work, Ateniese et al. [EuroS&P 2017] were the first to propose a redactable blockchain. Their approach focuses on permissioned blockchains and they showed how to change the content of a transaction without breaking the chaining among blocks by using special cryptographic hash functions (i.e., chameleon hash functions) and secure multi-party computation. We observe that the redaction technique of Ateniese et al. does not take into account the possibility that the blockchain supports smart contracts and that a redaction of a transaction might leave inconsistencies in the logic of the contracts, making some remaining non-redacted transactions invalid, and, more in general, the state of a smart contract inconsistent with the content of transactions. We find this choice rather limiting since decentralized and publicly verifiable computation guaranteed by smart-contract-enabled blockchains is necessary for modern (i.e., Web3) applications. To overcome the above limitations of the applicability of the redaction techniques of Ateniese et al., we propose a redaction technique with wider applicability that leverages succinct non-interactive arguments of knowledge (SNARKs) to realize what we call a proof-of-consistency .
The greatest advantage that Web3 applications offer over Web 2.0 is the evolution of the data access layer. Opaque, centralized services that compelled trust from users are replaced by trustless, decentralized systems of smart contracts. However, the public nature of blockchain-based databases, on which smart contracts transact, has typically presented a challenge for applications that depend on data privacy or that rely on participants having incomplete information. This has changed with the introduction of confidential smart contract networks that encrypt the memory state of active contracts as well as their databases stored on-chain. With confidentiality, contracts can more readily implement novel interaction mechanisms that were previously infeasible. Meanwhile, in both Web 2.0 and Web3 applications, the user interface continues to play a crucial role in translating user intent into actionable requests. In many cases, developers have shifted intelligence and autonomy onto the client, leveraging web technologies for computation, graphics, and networking. Web3’s reliance on such frontends has revealed a pain point though, namely that decentralized applications are not accessible to end users without a persistent host serving the web application. Here we introduce the Non-Fungible Program (NFP) model for developing self-contained frontend applications that are distributed via blockchain, powered by web technology, backed by encrypted databases, and controlled by confidential smart contracts. Access to frontend code, as well as backend services, is controlled and guaranteed by smart contracts according to the NFT ownership model, eliminating the need for a separate host. By extension, NFP applications bring interactivity to token owners and enable new functionalities—such as authorization mechanisms for oracles, supplementary web services, and overlay networks—in a secure manner. In addition to releasing an open-source software development kit for building NFPs, we demonstrate the utility of NFPs with an interactive Bayesian game implemented on Secret Network.
With the widespread adoption of Internet of Things (IoT) technologies in healthcare systems, security issues related to user privacy during data transmission and sharing have become increasingly prominent. To address these challenges, this paper proposes a medical privacy protection and secure sharing scheme based on Quantum Key Distribution (QKD). The scheme integrates multiple technologies, including blockchain, smart contracts, zero-knowledge proofs, and Chebyshev chaotic mapping, to ensure secure data sharing and access control among multiple communication entities. Compared with existing solutions, our approach enhances key management security through quantum keys and improves communication resilience against attacks by leveraging chaotic systems. User identity privacy is protected via zero-knowledge proofs. Under the random oracle model, the security of the proposed scheme is formally proven. Moreover, comparative experiments with existing protocols demonstrate the scheme's comprehensive advantages in terms of security and performance, evaluated across throughput, computational overhead, communication overhead, and storage overhead.
Existing electronic voting systems suffer from security concerns, identity theft, electoral fraud, and insufficient transparency in digital voting systems, which pose significant challenges to electoral integrity. Blockchain-based electronic voting systems provide immutability and decentralization. However, they are inappropriate for large-scale elections because of their inadequate consensus mechanisms, scalability issues, and security weaknesses. To provide an equitable electoral process, an electronic voting system must be scalable, secure, and efficient. This method requires real-time vote verification, secure vote recording to avert fraud, and voter authentication. This study introduces a blockchain-based smart contract electronic voting system (BCVS) to improve the security and efficiency of electronic voting. The three algorithms are employed by the proposed BCVS to safeguard and improve the electronic voting process via the utilization of smart contracts. These algorithms ensure the precise tabulation of results and establish a robust foundation for electronic voting by resolving disputes. The proposed approach ensures transparency, immutability, and a minimal likelihood of manipulation through the utilization of the prioritized delegated proof-of-stake (PDPoS) consensus mechanism. The PDPoS functions on Tier 3 scalable networks and diligently documents transactions on the blockchain to resolve critical challenges associated with electronic voting. The scalability and integrity of the proposed e-voting system are ensured through the implementation of a practical Byzantine fault tolerance algorithm. Improved voter authentication is accomplished by multi-factor authentication and elliptic curve digital signatures, reducing the dangers of unwanted access. Additionally, Nightshade sharding from the NEAR algorithm enhances scalability by partitioning the blockchain network into numerous smaller shards, facilitating parallel transaction processing. Consequently, throughput is markedly enhanced, and latency is diminished. The testing results indicate that the proposed BCVS achieves 100% confirmed transactions, 98% compatibility, 95% accuracy, and 95% audited votes. The proposed BCVS outperforms existing state-of-the-art systems in multiple essential domains, such as the volume of votes cast within a specified timeframe, precision, interoperability with other systems, quantity of confirmed transactions, auditability, and duration of vote counting.
This study introduces a framework that integrates blockchain, decentralized identity, and zero-knowledge proofs to enhance the trustworthiness and confidentiality of disaster information sharing. A sustainable model is proposed for real-world applications, supported by a prototype developed on the Decentralized Solutions for Humanity (DS4H) blockchain research network.
Emanuela Podda, Pol Hölzmer, Alexandre Amard, Johannes Sedlmeir · 5 authors
Zero-knowledge proofs allow the implementation of the data minimisation principle imposed by the GDPR in digital identity wallets and the related personal data transactions, therefore representing a reasonable option to be enforced by lawmakers.
In our research, we propose the first practically deployable construction of a multi-prover zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) protocol specifically tailored for restricted multiplication straight-line (RMS) programs, a computation model widely applicable in evaluating polynomials. Our protocol ensures input privacy, zero-knowledge, and security against fully malicious provers, all while eliminating the need for any inter-prover communication, making it highly suitable for distributed cloud environments. At the core of our approach is the introduction of the Restricted Quadratic Arithmetic Program model, an algebraic structure aligned with RMS semantics that enables provers to independently generate local proofs. We instantiate our framework using the Pinocchio protocol, resulting in a system that requires only 9 group elements per proof and 10 pairings for verification, nearly matching the efficiency of its single-prover counterpart. By leveraging our multi-prover zkSNARK protocol within a multi-server verification computation framework, we enable secure outsourcing of computations to the cloud of fully untrusted cloud servers. Compared to existing works, our protocol uniquely eliminates the need for any inter-server communication while achieving security even against adversaries controlling all servers.
Zeyad Ghaleb Al-Mekhlaf, Murtaja Ali Saare, Jalal Mohammed Hachim Altmemi, Mahmood A. Al-Shareeda · 9 authors
The rapid adoption of Internet of Medical Things (IoMT) devices enables real-time patient monitoring and remote diagnostics and has revolutionized healthcare delivery. Traditional cryptographic schemes like RSA and ECC, which rely on meaningful mathematical challenges, are under great threat from quantum computing, threatening sensitive medical data confidentiality and integrity. This paper proposes a quantum-resistant healthcare security framework based on lattice-based cryptographic primitives such as Learning With Errors (LWE), Ring-LWE (RLWE), and Short Integer Solution (SIS). To this end, we design a five-phase IoMT-friendly framework—Initialization, Registration, Authentication, Data Exchange, and Treatment—where each phase is backed up by lightweight cryptography primitives that can be easily implemented on the low-resource IoMT devices. Relative to the state-of-the-art lattice- and hash-based constructions, our framework involves 50-75% smaller ciphertext sizes, up to a 50% reduction of the communication overhead, and nearly 60% less in computational cost. Furthermore, the solution relies on zero-knowledge proofs, homomorphic encryption as well and attribute-based access control to guarantee strong security and privacy. Using the AVISPA tool, the framework is formally verified, showing its resistance against classical and quantum adversaries. Focusing on tangible healthcare threats, including data tampering and unlicensed access to patient diagnostics, this research paves the way for scalable, efficient, and quantum-resistant medical data protection. Our results pave the way for future investigations into secure post-quantum healthcare and IoT applications.
Zero-Knowledge Proofs (ZKPs) are critical for privacy-preserving techniques and verifiable computation. Many ZKP protocols rely on key kernels such as the SumCheck protocol and Merkle Tree commitments to enable their key security properties. These kernels exhibit balanced binary tree computational patterns, which enable efficient hardware acceleration. Although prior work has investigated accelerating these kernels as part of an overarching ZKP protocol, exploiting this common tree pattern remains relatively underexplored. We conduct a systematic evaluation of these tree-based workloads under different traversal strategies, analyzing performance on multi-threaded CPUs and the Multifunction Tree Unit (MTU) hardware accelerator. We introduce a hardware-friendly Hybrid Traversal for binary tree that improves parallelism and scalability while significantly reducing memory traffic on hardware. Our results show that MTU achieves up to $1478\times$ speedup over CPU at DDR-level bandwidth and that our hybrid traversal outperforms breadth-first search by up to $3\times$. These findings offer practical guidance for designing efficient hardware accelerators for ZKP workloads with binary tree structures.
Lambard Maxence, Cyrille Bertelle, D apos Amours Claude
In an increasingly complex contractual landscape, the demand for transparency, security, and efficiency has intensified. Blockchain technology, with its decentralized and immutable nature, addresses these challenges by reducing intermediary costs, minimizing fraud risks, and enhancing system compatibility. Smart contracts, initially conceptualized by Nick Szabo and later implemented on the Ethereum blockchain, automate and secure contractual clauses, offering a robust solution for various industries. However, their complexity and the requirement for advanced programming skills present significant barriers to widespread adoption. This study introduces a multi-level finite state machine model designed to represent and track the execution of smart contracts. Our model aims to simplify smart contract development by providing a formalized framework that abstracts underlying technical complexities, making it accessible to professionals without deep technical expertise. The hierarchical structure of the multi-level finite state machine enhances contract modularity and traceability, facilitating detailed representation and evaluation of functional properties. The paper explores the potential of this multi-level approach, reviewing existing methodologies and tools, and detailing the smart contract generation process with an emphasis on reusable components and modularity. We also conduct a security analysis to evaluate potential vulnerabilities in our model, ensuring the robustness and reliability of the generated smart contracts.
Xin Liu, Xinyuan Guo, Dan Luo, Liang Li · 9 authors
Federated learning promotes the development of cross-domain intelligent applications under the premise of protecting data privacy, but there are still problems of sensitive parameter information leakage of multi-party data temporal alignment and resource scheduling process, and traditional symmetric encryption schemes suffer from low efficiency and poor security. To this end, in this paper, based on the modified NTRU-type multi-key fully homomorphic encryption scheme, an asymmetric algorithm, a secure computation scheme of multi-party least common multiple and greatest common divisor without full set under the semi-honest model is proposed. Participants strictly follow the established process. Nevertheless, considering that malicious participants may engage in poisoning attacks such as tampering with or uploading incorrect data to disrupt the protocol process and cause incorrect results, a scheme against malicious spoofing is further proposed, which resists malicious spoofing behaviors and not all malicious attacks, to verify the correctness of input parameters or data through hash functions and zero-knowledge proof, ensuring it can run safely and stably. Experimental results show that our semi-honest model scheme improves the efficiency by 39.5% and 45.6% compared to similar schemes under different parameter conditions, and it is able to efficiently process small and medium-sized data in real time under high bandwidth; although there is an average time increase of 1.39 s, the anti-malicious spoofing scheme takes into account both security and efficiency, achieving the design expectations.
Yao Ma, Wen Yu Kon, J. O. Chu, Kevin Han Yong Loh · 6 authors
Identity verification is the process of confirming an individual's claimed identity, which is essential in sectors like finance, healthcare, and online services to ensure security and prevent fraud. However, current password/PIN-based identity solutions are susceptible to phishing or skimming attacks, where malicious intermediaries attempt to steal credentials using fake identification portals. Alikhani et al. [Nature, 2021] began exploring identity verification through graph coloring-based relativistic zero-knowledge proofs (RZKPs), a key cryptographic primitive that enables a prover to demonstrate knowledge of secret credentials to a verifier without disclosing any information about the secret. Our work advances this field and addresses unresolved issues: From an engineering perspective, we relax further the relativistic constraints from 60m to 30m, and significantly enhance the stability and scalability of the experimental demonstration of the 2-prover graph coloring-based RZKP protocol for near-term use cases. At the same time, for long-term security against entangled malicious provers, we propose a modified protocol with comparable computation and communication costs, we establish an upper bound on the soundness parameter for this modified protocol. On the other hand, we extend the two-prover, two-verifier setup to a three-prover configuration, demonstrating the security of such relativistic protocols against entangled malicious provers.