ABSTRACT Selfâsovereign identity management systems operate in open network environments and face security threats from semiâtrusted or malicious adversary models. In such environments, verifiable credentials are susceptible to attacks such as theft and forgery. In response to the privacy risks associated with verifiable credentials during issuance and revocation, this article proposes a privacy protection scheme for user information during the issuance and revocation processes of verifiable credentials in selfâsovereign identity management based on blockchain technology. First, a privacyâpreserving method that does not rely on a single identity provider and resists Sybil attacks has been designed using secure multiâparty computation cryptographic techniques. Second, the consortium blockchain committee nodes act as the issuer of verifiable credentials. By combining attribute commitments and zeroâknowledge proof techniques, the user's identity information is hidden, achieving the privacy protection goal during the issuance of verifiable credentials. Furthermore, in order to protect user privacy during the revocation of verifiable credentials (VCs), we employ a cryptographic accumulator technique to implement the revocation operation. This approach ensures the security of user privacy while effectively managing the revocation of credentials. Finally, this paper conducts a security analysis and performance evaluation of the proposed scheme. The results show that our scheme strikes a balance between security needs and time efficiency.
Zero-Knowledge Proofs (ZKPs) are a rapidly growing technique for privacy-preserving and verifiable computation.ZKPs enable one party (a prover: P) to prove to another (a verifier: V) that a statement is true or correct without revealing any additional information.This powerful capability has led to ZKPs being applied and proposed for application in blockchain technologies, verifiable machine learning, and electronic voting.However, ZKPs have yet to see widespread, ubiquitous adoption due to the exceptionally high computational complexity of the proving process.Naturally, there has been recent work to accelerate ZKP primitives and protocols using GPUs and ASICs.However, the protocols considered so far face one of two challenges: they require a trusted setup for each new application or generate large proofs with high verification costs, limiting their applicability in scenarios with numerous verifiers or strict verification time constraints.HyperPlonk is a state-of-theart ZKP protocol that supports both one-time, universal setup and small proof sizes/verification costs expected by publicly verifiable, consensus-based systems (e.g., blockchain).While HyperPlonk's setup and verifier properties are highly desirable, the proving phase is costly.A HyperPlonk prover must compute on large bitwidths (e.g., 255-381b) and polynomials (e.g., of degree 2 24 ), employs computationally (e.g., MSM) and bandwidth (e.g., SumCheck) intensive kernels, and the complete protocol comprises many steps, each constituting distinct kernels.We present an accelerator, zkSpeed, to
The round complexity of interactive proof systems is a key question of practical and theoretical relevance in complexity theory and cryptography. Moreover, results such as QIP = QIP(3) (STOC'00) show that quantum resources significantly help in such a task. In this work, we initiate the study of round compression of protocols in the bounded quantum storage model (BQSM). In this model, the malicious parties have a bounded quantum memory and they cannot store the all the qubits that are transmitted in the protocol. Our main results in this setting are the following: 1. There is a non-interactive (statistical) witness indistinguishable proof for any language in NP (and even QMA) in BQSM in the plain model. We notice that in this protocol, only the memory of the verifier is bounded. 2. Any classical proof system can be compressed in a two-message quantum proof system in BQSM. Moreover, if the original proof system is zero-knowledge, the quantum protocol is zero-knowledge too. In this result, we assume that the prover has bounded memory. Finally, we give evidence towards the âtightnessâ of our results. First, we show that NIZK in the plain model against BQS adversaries is unlikely with standard techniques. Second, we prove that without the BQS model there is no 2âmessage zero-knowledge quantum interactive proof, even under computational assumptions.
The seminal work of Goldreich and Krawczyk (SIAM Journal on Computing) shows that any constant-round public-coin interactive proof for languages not in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi mathvariant="sans-serif">B</mml:mi> <mml:mi mathvariant="sans-serif">P</mml:mi> <mml:mi mathvariant="sans-serif">P</mml:mi> </mml:mrow> </mml:mrow> </mml:math> cannot be black-box zero knowledge. Their result says nothing, however, about proofs (or arguments) of knowledge for languages in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi mathvariant="sans-serif">B</mml:mi> <mml:mi mathvariant="sans-serif">P</mml:mi> <mml:mi mathvariant="sans-serif">P</mml:mi> </mml:mrow> </mml:mrow> </mml:math>. As a special case, their work leaves open the question of whether Schnorr's protocol for proving knowledge of discrete logarithms in cyclic groups is black-box zero knowledge. In this work we focus on the zero knowledge of proofs of knowledge, centering on Schnorr's protocol as a prominent example. We prove two lower bounds, ruling out two different classes of simulators through which Schnorr's protocol can be proven zero knowledge: We prove that if a relation <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>â</mml:mi> </mml:mrow> </mml:math> has a public-coin interactive proof of knowledge that is black-box zero knowledge and this protocol is compatible with the Fiat-Shamir transform in the random oracle model, then <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>â</mml:mi> </mml:mrow> </mml:math> must be efficiently searchable. As an immediate corollary, we deduce that Schnorr's protocol cannot be black-box zero knowledge in groups in which discrete log is hard. We define a new class of simulators for Schnorr's protocol, which we call generic simulators. A generic simulator is one that works in any cyclic group, and does not use the representation of the specific group in which Schnorr's protocol is instantiated. We prove that Schnorr's protocol cannot have generic simulators. As an additional contribution, we generalize the original lower bound of Goldreich and Krawczyk, to prove that a language not in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi mathvariant="sans-serif">B</mml:mi> <mml:mi mathvariant="sans-serif">P</mml:mi> <mml:mi mathvariant="sans-serif">P</mml:mi> </mml:mrow> </mml:mrow> </mml:math> cannot have an interactive proof (not necessarily of knowledge) that is both black-box zero knowledge and compatible with the Fiat-Shamir transform in the random oracle model. In conjunction with recent works, this extends the Goldreich-Krawczyk lower bound to public-coin protocols that are not constant-round but have round-by-round soundness, including the parallel repetition of any public-coin interactive proof.
Michele OrrĂč, George Kadianakis, Mary Maller, Greg Zaverucha
A fundamental challenge in zero-knowledge proof systems is implementing operations that are âforeignâ to the underlying constraint system, in that they are arithmetic operations with a different modulus than the one used by the proof system. The modulus of the constraint system is a large prime, and common examples of foreign operations are Boolean operations, field arithmetic, or public-key cryptography operations. We present novel techniques for efficiently embedding such foreign arithmetic in zero-knowledge, including (i) equality of discrete logarithms across different groups; (ii) scalar multiplication without requiring elliptic curve operations; (iii) proving knowledge of an AES encryption. Our approach combines rejection sampling, sigma protocols, and lookup protocols. We implement and provide concrete benchmarks for our protocols.
Blockchain-driven financial innovation in Hong Kong from 2018 to 2024 has transformed cross-border payment systems through strategic regulatory frameworks and public-private collaboration. Key developments include the e-HKD Pilot Programme, integration with China's digital yuan, and pioneering CBDC initiatives like Project mBridge. This review analyzes technological implementations (Layer-2 solutions, zero-knowledge proofs), regulatory evolution across three distinct phases, and economic impacts including 38\% cost reduction in SME transactions. We examine Hong Kong's unique position bridging China's financial infrastructure with global markets while navigating geopolitical tensions and compliance challenges. The study provides quantitative metrics from 50+ corporate disclosures and regulatory documents, establishing a model for hybrid governance systems in financial technology adoption.
Nishant Jagannath, Christopher Kevin Wong, Braden Mcgrath, Md. Faruque Hossain · 7 authors
The rapid advancement of artificial intelligence (AI) has brought about sophisticated models capable of various tasks ranging from image recognition to natural language processing. As these models continue to grow in complexity, ensuring their trustworthiness and transparency becomes critical, particularly in decentralized environments where traditional trust mechanisms are absent. This paper addresses the challenge of verifying personalized AI models in such environments, focusing on their integrity and privacy. We propose a novel framework that integrates zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) with Chainlink decentralized oracles to verify AI model performance claims on blockchain platforms. Our key contribution lies in integrating zk-SNARKs with Chainlink oracles to securely fetch and verify external data to enable trustless verification of AI models on a blockchain. Our approach addresses the limitations of using unverified external data for AI verification on the blockchain while preserving sensitive information of AI models and enhancing transparency. We demonstrate our methodology with a linear regression model predicting Bitcoin prices using on-chain data verified on the Sepolia testnet. Our results indicate the framework's efficacy, with key metrics including proof generation taking an average of 233.63 seconds and verification time of 61.50 seconds. This research paves the way for transparent and trustless verification processes in blockchain-enabled AI ecosystems, addressing key challenges such as model integrity and model privacy protection. The proposed framework, while exemplified with linear regression, is designed for broader applicability across more complex AI models, setting the stage for future advancements in transparent AI verification.
Debugging and auditing zero-knowledge-compatible smart contracts remains a significant challenge due to the lack of source mapping in compilers such as zkSolc. In this work, we present a preliminary source mapping framework that establishes traceability between Solidity source code, LLVM IR, and zkEVM bytecode within the zkSolc compilation pipeline. Our approach addresses the traceability challenges introduced by non-linear transformations and proof-friendly optimizations in zero-knowledge compilation. To improve the reliability of mappings, we incorporate lightweight consistency checks based on static analysis and structural validation. We evaluate the framework on a dataset of 50 benchmark contracts and 500 real-world zkSync contracts, observing a mapping accuracy of approximately 97.2% for standard Solidity constructs. Expected limitations arise in complex scenarios such as inline assembly and deep inheritance hierarchies. The measured compilation overhead remains modest, at approximately 8.6%. Our initial results suggest that source mapping support in zero-knowledge compilation pipelines is feasible and can benefit debugging, auditing, and development workflows. We hope that this work serves as a foundation for further research and tool development aimed at improving developer experience in zk-Rollup environments.
Suhyeon Lee, Euisin Gee, Najmeh Soroush, Muhammed Ali Bingol · 5 authors
Simple commit-reveal beacons are vulnerable to last-revealer strategies, and existing descriptions often leave accountability and recovery mechanisms unspecified for practical deployments. We present Commit-Reveal$^2$, a layered design for blockchain deployments that cryptographically randomizes the final reveal order, together with a concrete accountability and fallback mechanism that we implement as smart-contract logic. The protocol is architected as a hybrid system, where routine coordination runs off chain for efficiency and the blockchain acts as the trust anchor for commitments and the final arbiter for disputes. Our implementation covers leader coordination, on-chain verification, slashing for non-cooperation, and an explicit on-chain recovery path that maintains progress when off-chain coordination fails. We formally define two security goals for distributed randomness beacons, unpredictability and bit-wise bias resistance, and we show that Commit-Reveal$^2$ meets these notions under standard hash assumptions in the random-oracle model. In measurements with small to moderate operator sets, the hybrid design reduces on-chain gas by more than 80% compared to a fully on-chain baseline. We release a publicly verifiable prototype and evaluation artifacts to support replication and adoption in blockchain applications.
Muhammad Jawad, âȘMahmood A. Al-ShareedaâŹâ, Omar Yawez Mustafa Mustafa, Mohammed Amin Almaiah · 5 authors
Analysis of repeated attack signatures is important because of the rapid evolution of the Social Internet of Vehicles (SIoV). However, threats such as replay attacks, session hijacking, and key reuse make secure communication between vehicles, roadside units (RSUs), and the fog node difficult. Traditional models for authentication are limited by computational overhead and lack quick key revocation. In response to these challenges, we propose a hybrid cryptographic authentication scheme that combines a Zero-Knowledge Proof (ZKP) with AES-GCM encryption. Our protocol implements a dynamic key revocation mechanism to avoid rogue and session key migration, minimizing re-authentication delay. Security analysis in the Real-Oracle Random (ROR) model shows that it is not vulnerable to impersonation or replay attacks. Evaluations demonstrate decreases of 58% in authentication latency while achieving 45% and 72% improvements in communication and computation efficiency, respectively. Our approach is also scalable and secure, providing SIoV with higher reliability for automotive applications in the vehicular networks of the future.
The proliferation of numerous portable mobile devices has made mobile crowd-sensing (MCS) systems a promising new trend. Traditional MCS systems typically outsource sensing tasks to the data aggregator (e.g., cloud server). They collect and analyze the provided sensing data through an appropriate truth discovery (TD) method to identify valuable data sets. However, existing privacy-preserving MCS systems lack transparency, enabling data aggregators to deviate from the specified protocols and allowing malicious users to provide false or invalid sensing data, thereby contaminating the resulting data sets. The lack of transparency and public verifiability in MCS systems undermines widespread adoption by preventing data requesters from confidently verifying data integrity and accuracy. To address this issue, we propose a transparent and privacy-preserving mobile crowd-sensing system with truth discovery (TP-MCS) constructed using zero-knowledge proof (ZKP) and the Merkle commitment tree. This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. Furthermore, theoretical analysis and extensive experiments demonstrate that this scheme is secure and efficient.
Following the emergence of the COVID-19 pandemic, electronic voting has gradually become an inseparable part of people's lives. However, it has also raised a series of severe privacy and trust challenges. The immutable and publicly transparent characteristics of blockchain are a perfect fit for the development of electronic voting systems, effectively eliminating voters' concerns about ballot tampering.At the same time, zero-knowledge proofs enable the prover to show they possess certain information to the verifier, without disclosing the actual details. It is important to note that with the rapid development of quantum technology, traditional cryptographic schemes face unprecedented security threats. To address this challenge, We present a quantum-resistant blockchain solution for electronic voting, incorporating zero-knowledge proofs. Compared to conventional elliptic curve-based zero-knowledge proof schemes, our proposed solution is based on RLWE, ensuring voter privacy, and uses BFV fully homomorphic encryption technology to implement a blockchain-based electronic voting protocol, ensuring the systemâs high availability, security, and anonymous voting. Security analysis and performance testing, along with comparisons to existing similar solutions, show that our scheme has advantages in terms of security and robustness, making it highly practical.
The global shift towards sustainable transportation necessitates efficient and secure payment systems for electric vehicle (EV) charging on electrified roads. Current blockchain-based payment infrastructures face high transaction costs, inefficiencies, and security vulnerabilities, impeding EV adoption. To address these challenges, we propose a blockchain-based Vehicle Payment System (VPS) tailored for electrified roads. VPS integrates a hybrid consensus mechanism combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) for secure, decentralized, and efficient transaction validation. Scalability is enhanced through sharding, which distributes transaction load, while Zero-Knowledge Proofs (ZKPs) ensure transaction confidentiality, and multi-signature transactions provide additional security. State channels further optimize performance by enabling off-chain transactions, reducing congestion, and increasing throughput. Unlike prior research, which often neglects scalability, privacy, and real-time performance holistically, VPS achieves under 3000 ms latency for invoke transactions, under 450 ms for queries with 1000 users, and a throughput of approximately 1100 transactions per second (TPS) at a send rate of 1300. These advancements establish VPS as a scalable, efficient payment solution for EV charging, supporting the transition to green mobility and informing sustainable infrastructure policies.
Distributed Ledger Technologies (DLTs), including Blockchain, have revolutionized financial systems by offering decentralized, transparent, and secure mechanisms for data management and transactions. However, for these systems to maintain integrity and protect sensitive financial data, robust cryptographic techniques are essential. Cryptography ensures data confidentiality, authenticity, integrity, and non-repudiation, which are critical for the security of financial transactions in DLTs. This article examines the role of cryptographic protocols such as hashing, digital signatures, asymmetric encryption, and zero-knowledge proofs in safeguarding distributed ledgers. Furthermore, we explore their applications in securing financial transactions, preventing fraud, ensuring compliance, and enhancing the overall reliability of DLTs in financial systems. The discussion also delves into the challenges of cryptographic security in the face of emerging threats and the potential impact of quantum computing on existing cryptographic protocols.
The integration of blockchain , Internet of Things devices, and distributed energy resources is revolutionizing peer-to-peer energy trading by enabling decentralized, efficient, and transparent transactions. However, existing solutions face challenges related to privacy, interoperability, and scalability. This paper presents PrGChain, a privacy-preserving blockchain-enabled energy trading framework within the smart grid, incorporating a decentralized ZKOracle to securely connect blockchain networks with off-chain energy data sources . The proposed ZKOracle employs zero-knowledge proofs to verify energy data without exposing sensitive information , ensuring compliance with privacy regulations, while leveraging a distributed network of oracle nodes for enhanced reliability and interoperability. To improve security and efficiency, PrGChain utilizes smart contracts , decentralized applications (dApps), and leverages stablecoins to mitigate cryptocurrency volatility. Performance evaluations demonstrate that our system achieves improved decentralization and privacy without sacrificing efficiency. This is particularly true when deployed on Layer 2 blockchain networks like Polygon, where transaction latency and costs are significantly reduced.
John C. Kolesar, Shan Ali, Timos Antonopoulos, RuĆŸica PiskaÄ
Zero-knowledge (ZK) protocols enable software developers to provide proofs of their programsâ correctness to other parties without revealing the programs themselves. Regular expressions are pervasive in real-world software, and zero-knowledge protocols have been developed in the past for the problem of checking whether an individual string appears in the language of a regular expression, but no existing protocol addresses the more complex PSPACE-complete problem of proving that two regular expressions are equivalent. We introduce CrĂȘpe , the first ZK protocol for encoding regular expression equivalence proofs and also the first ZK protocol to target a PSPACE-complete problem. CrĂȘpe uses a custom calculus of proof rules based on regular expression derivatives and coinduction, and we introduce a sound and complete algorithm for generating proofs in our format. We test CrĂȘpe on a suite of hundreds of regular expression equivalence proofs. CrĂȘpe can validate large proofs in only a few seconds each.
ABSTRACT Ensuring the security and privacy of sensitive health data in Internet of Things (IoT)âbased healthcare systems (HCS) is a critical challenge. This paper proposes a robust security framework by integrating blockchain mechanisms and deep learning (DL) approaches to enhance security and data privacy. The proposed framework leverages the Ethereum blockchain with zero knowledge proof (ZKP) to ensure data integrity and confidentiality, while the interplanetary file system (IPFS) provides secure and efficient data storage. Additionally, a novel AtâGANâBiLSTM model is introduced for intrusion detection by combining the attention mechanism, generative adversarial networks (GAN) and bidirectional long shortâterm memory (BiâLSTM) to improve detection accuracy and also help to enhance model robustness. The proposed model is evaluated by two different benchmark datasets, namely CICIDSâ2018 (D1) and ToNâIoT (D2), achieving accuracies of 99.9% and 99.1%, respectively. Comparative investigation shows that the proposed approach reduces false alarm rates (FAR) and performs better than current models in identifying impersonation, insider, and manâinâtheâmiddle (MITM) attacks. By integrating blockchain and DL, the proposed framework significantly enhances intrusion detection, data security, and overall system resilience, addressing key vulnerabilities in IoTâbased healthcare security.
The protection of personal privacy has become a paramount issue in the field of data science, with its significance continuously rising. Differential privacy technology has garnered significant attention for its effectiveness in preserving individual privacy. However, the implementation of differential privacy relies on a degree of trust in the entities or individuals executing the algorithms. This paper proposes an innovative solution: a verifiable differential privacy mechanism based on zero-knowledge proofs. This approach integrates differential privacy with zero-knowledge proof technology to not only verify the correctness of the differential privacy techniques but also enhance the transparency and reliability of the algorithms. Additionally, we have designed a publicly verifiable data release scheme that integrates commitment mechanisms and range proofs, ensuring that the range of published data noise does not exceed predetermined thresholds, thereby ensuring the utility of the data. Compared to other verifiable differential privacy solutions, our approach is unique in that it does not rely on the number of participants but is solely dependent on the precision of the data. This means that our computational cost will not increase with the addition of more participants. Finally, we conducted a performance evaluation of the solution, which only took 700ms to complete a single verification. On average, there was a 6% reduction in expectation and a 40% reduction in variance, demonstrating the enhancement of its data utility and the feasibility and effectiveness in practical applications.
This article presents a comprehensive framework for implementing privacy-preserving credit card reward systems using Secure Multi-Party Computation (SMPC) technologies. Traditional reward architectures require extensive sharing of sensitive transaction data across multiple entities, creating significant privacy risks, security vulnerabilities, and regulatory compliance challenges. It leverages cryptographic advances to enable card issuers, payment networks, and merchant partners to collaborate on reward calculations,fraud detection, and personalized offers without revealing sensitive transaction details to one another. The article explores the evolution of privacy-preserving technologies in financial systems, comparing Fully Homomorphic Encryption, Zero-Knowledge Proofs, and SMPC approaches. A detailed case study of a travel rewards program implementation demonstrates how this framework ensures data remains protected throughout the entire process while maintaining the performance characteristics necessary for production deployment. The system provides comprehensive privacy protection, enhances fraud detection capabilities through secure collaboration, and facilitates compliance with evolving privacy regulations.Performance evaluations confirm the practical viability of the article, with minimal latency impact, strong scalability characteristics, and robust security guarantees. It contributes to the growing field of privacy-enhancing technologies for financial services and offers a viable solution to balance analytical utility with privacy protection in consumer-facing applications.
In this work, we address the evolutionary computation in programmable cryptography on blockchain technologies for the first time in the literature. For that, we propose a novel privacy-preserving and decentralized protocol ([email protected]) where the evolutionary computation model is public while the user inputs/outputs (i.e. the current and next populations) are private. The protocol relies on the transitions between the public-domain (i.e. contract-domain) and the private-domain (i.e. evolutionary-domain) to be secure. We perform an experimental study using two popular benchmark problems to measure the blockchain gas consumption, zero-knowledge proof generation/verification times and zero-knowledge proof size.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Multi-Party Computation (MPC), as a distributed computing paradigm, is considered to be a potential solution for providing privacy-preserving for applications following the client-server model. However, traditional MPC solutions cannot satisfy the publicly verifiable requirement of the client-server model. In this paper, we propose a blockchain-based verifiable MPC solution using Pedersen's threshold secret sharing and Lifted ElGamal encryption. We first build a data distribution method using Pedersen's threshold secret sharing and symmetric encryption to protect the privacy of inputs while ensuring robustness. Then, we propose a result processing algorithm using Lifted ElGamal encryption to safeguard the privacy of the outputs. Finally, we employ non-interactive zero-knowledge proof and Pedersen commitment to publicly verify the correctness of the encrypted outputs in the smart contract, enabling the detection of malicious parties. Theoretical analysis indicates that the proposed method can publicly verify the correctness of outputs without revealing plain-text inputs and outputs, which satisfy the privacy-preserving requirements of the client-server model. Experimental evaluations have demonstrated that our proposed approach is efficient regarding computation overhead, communication overhead, and response time in the output verification phase while achieving stronger privacy and robustness.
Sang-Hyeon Park, Jeonghyuk Lee, Seunghwa Lee, Jung Hyun Chun · 8 authors
Merging Internet (web2) identities with blockchain (web3) identities is increasingly important for enhancing user experience and ensuring regulatory compliance. However, conventional solutions that map web2 identities to web3 accounts often lead to privacy concerns and fragmented identifiers across networks. To address these challenges, we propose a new identity scheme named Address Abstraction (AA), which redefines blockchain address and signing systems while preserving key properties: uniqueness, immutability, and privacy-preservation. This approach eliminates the limitations of chain-specific identity systems, enabling users to interact with multiple blockchains using their web2 certificates and unified identifiers. This chain-agnostic identifier also promotes cross-chain compatibility. We further present Zero-Knowledge Address Abstraction (zkAA), an implementation of AA that uses zero-knowledge proofs to uphold AA's core properties. Additionally, a proof aggregation technique combines multiple proofs into one, achieving approximately 5.5 times gas cost savings during verification in real-world scenarios. As of August 2024, zkAA with proof aggregation incurs an additional cost of only $0.66 per transaction on Ethereum.
Background: Even while traditional Raft is effective at leader election and log replication, it is not appropriate for sensitive applications like supply chains, financial systems, or healthcare because it lacks built-in privacy safeguards. Materials and Methods: A privacy-preserving Raft consensus method is proposed to solve the privacy issues that occur when private information is transferred between nodes in a distributed system such as a blockchain. Raft itself, by default, does not provide any steps toward ensuring data confidentiality during consensus. By employing privacy-preserving cryptographic techniques like homomorphic encryption and zero-knowledge proofs, nodes can reach consensus while keeping sensitive data private. Results: Traditional Raft performs much better in scenarios where performance matters, while Privacy-Perving Raft works better in a sensitive application to privacy (the average of write throughput is 5% lower than that of traditional Raft) and CPU is 40-60%. Conclusion: Based on the gained privacy by some computational costs, it will be valid to draw the conclusion that this works for privacy-sensitive applications within decentralized systems with these performance and security analyses.
In response to the issues of high transaction transparency and regulatory difficulties in blockchain account-model transactions, this paper presents a supervised blockchain anonymous transaction model based on certificateless signcryption aimed at ensuring secure blockchain transactions while minimizing both computational and communication overhead. During the transaction process, this approach utilizes certificateless public key signcryption without bilinear pairs to generate anonymous user identities, achieving strong anonymity of user identities and confidentiality of transaction amounts. It employs the Paillier homomorphic encryption algorithm to update transaction amounts and uses the FO commitment-based zero-knowledge proof scheme to validate transaction legality. Additionally, adopting a publicly verifiable secret threshold sharing scheme for hierarchical regulatory authority reduces the security risk of a single regulator storing the regulatory key. This model not only meets the privacy and timely update requirements of account-based blockchain transactions but also effectively regulates abnormal transactions. Rigorous security analysis and proofs demonstrate that this model possesses excellent anonymity, traceability, forward security, and backward security. When compared to similar schemes, the computational cost is reduced by at least 33.18%, effectively fulfilling the requirements for security.