Aiming at the existing problems of insufficient security and poor scalability of existing blockchain data management solutions for the Internet of Vehicles, we use IPFS distributed technology to design a data privacy protection method for the Internet of Vehicles based on zero-knowledge proof. First, we use the Schnorr identity authentication protocol, a classic non-interactive zero-knowledge proof scheme, to complete the authentication and authorization of vehicles by RSU under the premise of protecting vehicle privacy. Secondly, we store encrypted data on IPFS-based edge distributed servers, and use Hyperledger Fabric to store authentication records and data indexes, which solves the problem of insufficient blockchain scalability. Finally, in order to improve the controllability and security of the data, we use the proxy re-encryption mechanism to manage the Symmetric-key of the data, and let RSU act as a proxy to perform re-encryption. Compared with the existing schemes, the proposed scheme reduces the communication overhead of the vehicle identity authentication phase, which is only 1728 bits. And while improving the scalability of the blockchain, it also enhances the privacy protection of the data of the Internet of Vehicles.
With the emergence of blockchain and smart contracts, traditional digital applications 1 such as identity management, supply chain management, banking and finance etc. are expected 2 to be transformed into Decentralized Applications (DApps). Blockchain interoperability is a new 3 and exciting aspect of blockchain technology that is quickly gaining popularity in many industries. 4 However, the widespread use of blockchains has not yet been achieved because DApps running on the 5 blockchain using smart contracts require access to authentic off-chain data. Users are more conscious 6 of their personal data privacy and consequently are more reluctant to share their data, posing a 7 challenge in the adoption of DApps. To solve this challenge, we present a novel quantum-resistant, 8 data authentication and zero-knowledge proof scheme named zk-DASTARK. Based on our proposed 9 scheme, we present a novel framework which can be used to feed authenticated off-chain data to 10 DApps without compromising user data privacy. Our proposed framework is quite efficient against 11 well-known off-chain data authentication frameworks. The proposed framework is implemented on 12 state-of-the-art quantum-resistant IOTA Blockchain and is highly efficient as it takes approximately 13 60 ms to generate a proof and approximately 10 ms to verify a proof.
Marta Irene García Cid, Dileepsai Bodanapu, Rodrigo Martín Sánchez-Ledesma, Laura Ortiz Martín · 8 authors
This work presents a new scheme based on a quantum zero-knowledge proof for identity authentication. The novelty of this research is the migration of the classical concept Zero-knowledge into the quantum cryptographic framework that, to the best of our knowledge, has never been explored. This approach allows us to take advantage of the principles of quantum mechanics to build a protocol, which is secure against quantum computer attacks, for authenticating several users having access to the same network node. The protocol has been designed, its security analysed and implemented in modified Quantum Key Distribution devices. Two scenarios have been analysed experimentally, the first being both the prover and the verifier honest players, and the second case being the prover a malicious player, the latter demonstrating a notable increase in the quantum bit error rate that prevents a fraudulent authentication.
Stake voting is a voting system where the vote cast by a voter is proportional to their account balance/asset. We propose the first implementation of an efficient, fully-decentralized, universally-verifiable stake voting system with perfect voter privacy. Our contribution is two-fold. First, we design a zero knowledge proof (ZKP) for equality of plaintexts of 2 different ciphertexts encrypted under different encryption schemes, one under ElGamal encryption scheme and another under Paillier encryption scheme. We believe that ours is the first ZKP design for this problem statement. Second, we implement a publicly-verifiable Stake voting system using the ZKP design by enforcing perfect privacy to voters and correctness of election results. Our voting system is applicable to many situations like shareholder/stockholder voting in companies. We have also provided the financial and computational breakdown feasibility of our implementation on Ganache local blockchain.
Zero-knowledge proof (ZKP) is a popular cryptographic strategy for building a trusted environment, which can be applied to blockchain, electronic voting, and other scenarios. However, ZKP involves a number of computationally intensive operations that limit its widespread adoption in time-sensitive practical applications. The multi-scalar multiplication (MSM) dominates the computations and takes over 70% of the total computation time. This paper proposes a GPU-based acceleration method for ZKP by designing several optimization techniques for MSM. First, this paper constructs a formal mathematical formula of the Pippenger algorithm, which provides a theoretical optimization framework for MSM. Second, by parallelizing the prefix sum, the time complexity of the bucket reduction part of MSM is reduced from $\mathcal{O}\left( {3 \times {2^C}} \right)$ to $\mathcal{O}\left( {2 \times {2^C}} \right)$. Finally, this paper also analyzes the influence of group size on the final calculation time under different data scales and gives a suitable range of group sizes. Compared to the state-of-the-art method, our method can achieve 1.01× to 1.12× for throughput.
Zijun Zhan, Yaxian Dong, Daniel Mawunyo Doe, Yuqing Hu · 8 authors
With the remarkable progress in teleoperation, physical fitness-based gender bias has become negligible within the construction sector. Nonetheless, the labor market remains male-dominated, posing tremendous unfairness toward females. In light of this, we developed a two-phase recruitment framework that utilizes blockchain, zero-knowledge proofs (ZKPs), deep reinforcement learning (DRL), and contract theory, aiming to enhance fairness, transparency, and automation. First, we devised a resume screening approach independent of gender to ensure fairness and alleviate gender bias in candidate assessment, by leveraging blockchain and ZKPs. In the second phase, we introduce a recruitment process that combines blockchain and DRL-based contract theory. This integration successfully mitigates gender bias that may arise from the self-disclosure property of contract theory. To evaluate the effectiveness of our proposed approach, we conducted comprehensive simulations from various dimensions. The results demonstrated the robustness and superiority of our method.
Yuping Yan, George Shao, Dennis Song, Mason Song · 5 authors
Blockchain transactions have gained widespread adoption across various industries, largely attributable to their unparalleled transparency and robust security features. Nevertheless, this technique introduces various privacy concerns, including pseudonymity, Sybil attacks, and potential susceptibilities to quantum computing, to name a few. In response to these challenges, innovative privacy-enhancing solutions like zero-knowledge proofs, homomorphic encryption, and stealth addresses (SA) have been developed. Among the various schemes, SA stands out as it prevents the association of a blockchain transaction's output with the recipient's public address, thereby ensuring transactional anonymity. However, the basic SA schemes have exhibited vulnerabilities to key leakage and quantum computing attacks. To address these shortcomings, we present a pioneering solution - Homomorphic Encryption-based Dual-Key Stealth Address Protocol (HE-DKSAP), which can be further extended to Fully HE-DKSAP (FHE-DKSAP). By leveraging the power of homomorphic encryption, HE-DKSAP introduces a novel approach to safeguarding transaction privacy and preventing potential quantum computing attacks. This paper delves into the core principles of HE-DKSAP, highlighting its capacity to enhance privacy, scalability, and security in programmable blockchains. Through a comprehensive exploration of its design architecture, security analysis, and practical implementations, this work establishes a privacy-preserving, practical, and efficient stealth address protocol via additively homomorphic encryption.
Zero-Knowledge Proofs (ZKPs) ensure privacy in sensitive applications, including blockchains, anonymous verifiable voting, and secure auctions. In recent years, a number of ZKP implementations have been presented. The diversity of available solutions raises the need to compare the implementations to discover their strengths and weaknesses. Unfortunately, the collection of ZKP implementations is highly heterogeneous, aggravating a just comparison between different implementations. Hence, there is a strong need for a benchmarking solution to measure and collect metrics to compare different ZKP implementations. Therefore, we design and implement a benchmarking solution for ZKPs. The proposed solution allows users to gather performance metrics of different ZKP implementations and aids the decision-making process of selecting the right implementation for a specific use case. The system is highly configurable and allows the integration of novel ZKP systems. Further, we demonstrate the solution’ s applicability by benchmarking two popular ZKP implementations.
Abdelrahman Mustafa El-Feky, A. Alavudeen Basha, Karim Mohamed Gomaa, Youssef Ashraf El-Etreby · 6 authors
Authentication is the indispensable key process of identifying a user's identity who's using a service, traditionally using passwords. As the number of services grew, password managers have, since then, been used to manage users' passwords. Traditional centralized password managers have proven their inefficacy in securing a user's password and have failed to keep up with the newer security demands, owing to their centralized architecture and reliance on quantum-vulnerable authentication methods. In this paper, an authentication manager is proposed whose model deploys a zero-trust and zero-knowledge architecture, blockchains, hardware authentication, and quantum-proof algorithms to keep up with the modern security demands. The proposed model has eliminated the single point of failure using decentralization, utilized passkeys, and deployed quantum-proof cryptographic algorithms.
Bug bounty programs play a pivotal role for companies in identifying vulnerabilities within their digital products through collaborations with a global network of security researchers. In this paper, we introduce the bug bounty trilemma challenge—a concept that entails the delicate balance of security, privacy, and rewards within these programs. We then delve into “Bounty 3.0” as an innovative approach, harnessing the transformative potential of blockchain and Zero-Knowledge Proofs (ZKPs). Blockchain technology ensures un-paralleled transparency, decentralization, and immutability in bug reports and token-based bounties, while ZKPs establish a secure, anonymous, and confidential reporting process that safeguards privacy. This approach acts as a bridge, seamlessly connecting traditional bug bounty programs with block-chainWeb3 solutions, effectively addressing the bug bounty trilemma.
Democracy, a system of governance founded on the principle of citizen representation, has become prevalent worldwide. Elections are crucial components of democratic systems, and it is essential to guarantee transparent, secure, and impartial election processes to maintain citizens’ trust in the government. However, the lack of transparency and security in democratic elections has raised concerns about their legitimacy and fairness. Creating a secure e-voting system that provides fairness, anonymity, transparency, and flexibility has been a significant challenge, despite the increasing adoption of technology worldwide. Electronic Voting Machines (EVMs) offer time and effort-saving benefits compared to traditional paper ballots but are susceptible to challenges that compromise the integrity of the electoral process. To address these challenges, we have proposed a Blockchain based E-voting system. This system provides a secure, decentralized platform where votes are recorded on an immutable ledger. It enables real-time vote counting, overcoming the time-consuming and error-prone nature of traditional methods. The proposed approach involves using Hyperledger Fabric and Chaincode to develop a scalable, maintainable, and cost-effective e-voting system within a customized private Blockchain network. This system adheres to the principles of Zero Knowledge Proof, ensuring accurate vote accounting and safeguarding the credibility and legitimacy of democratic elections.
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Blockchain is known for its potential to create high transparency, decentralization, and unprecedented security. However, it is still confronted with scalability problems which pose a significant barrier to its wide adoption. Scalability constraints result in high transaction fees, high latency, and low throughput in many blockchain networks. To handle this bottleneck, several scaling solutions have been developed. Recent research has explored hybridization of scaling solutions, paving the way for major scaling innovations. This article analyzes how parallel and aggregated scaling methods can be integrated to achieve exponential scalability in blockchain networks. We examine fundamental concepts, techniques, implementation strategies, and then propose ZKShard, a paradigm based on zero-knowledge proofs and sharding. In the proposed system, key components such as parallelization, communication, aggregation, user experience and interaction were examined. We also explored ZKShard applications in domains such as Decentralized Finance (DeFi), New Financial Technologies (NFTs), and Supply Chain Management. Finally, we discussed research challenges and future directions based on security, cross-shard communication, data availability, smart contracts, and consensus mechanism.
Abhijeet R. Raipurkar, Shreyas Bobde, Anurag Tripahi, Mohit Sahu
A digital identity represents an external entity, be it a person, business, program, or object, and serves as the foundation for automatic access to computer-based services and interpersonal interactions. Despite years of research, the challenge of reliable internet connectivity for digital identification remains unresolved. In this paper, we propose a blockchain-based solution for digital identification in situations of mutual mistrust. Unlike current identity management systems that rely on centralized storage, our recommendation is a blockchain-based, self-sovereign identity (SSI) platform where true identities of customers/users are held in their respective web applications, utilizing decentralized storage. The identification information is validated using the Zero Knowledge Proof (ZKP) technique offered by SSI-based platforms, ensuring privacy and security. The proposed approach in this paper serves as a digital identity wallet, empowering users to verify their identities using blockchain technology and an SSI-based approach, resulting in a decentralized, ZKP-verified, and immutable identity. By addressing the issues of data immutability, traceability, and centralized control often associated with traditional identification systems, the proposed approach offers a robust and versatile model of proven claims that can be verified by external sources. Different implementations of claim formation and verification, showcasing sub-second performance is presented in the paper.
Blockchain Technology Applications and Security
Cloud Data Security Solutions
Advanced Steganography and Watermarking Techniques
In today’s era, over 90 percent of the population is involved in digital transactions. This increased adoption of digital payments has raised concerns about security problems, such as insecure and fraudulent transactions, phishing, replay attacks, and password attacks. Authentication and data privacy play a major role in addressing these security challenges.This paper proposes a secure mechanism to perform authentication using the Zero Knowledge Proof (ZKP) algorithm in Blockchain. Zero Knowledge Proof (ZKP) allows users to prove that they possess certain knowledge or information without revealing that knowledge or information to the verifier. This makes Zero Knowledge Proof (ZKP) ideal for privacy-preserving authentication, which is essential for protecting sensitive user data. This enables storing the record of all the payment history keeping transaction tracking and makes the record storage immutable and tamper-proof. With added features of better payment authentication and payment tracking, the system mitigates any chances of corruption, unauthorized transactions, and payment discrepancies. This leads to a faster and more transparent end-to-end process. The scheme is demonstrated using a funds flow process between vendors by implementing a smart contract. The system accuracy is tested by performing audit analysis using the SolidityScan tool achieving a 94.44 score. The overall implementation shows that the proposed scheme outperforms several attacks preventing unauthorized access and providing data privacy.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Peer review lies at the core of the academic process, but even well-intentioned reviewers can still provide noisy ratings. While ranking papers by average ratings may reduce noise, varying noise levels and systematic biases stemming from ``cheap'' signals (e.g. author identity, proof length) can lead to unfairness. Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information. We ask reviewers to predict others' scores and use these predictions for calibration. Assuming reviewers adjust their predictions according to the noise, we demonstrate that the calibrated score results in a more robust ranking compared to average ratings, even with varying noise levels and biases. In detail, we show that the error probability of the calibrated score approaches zero as the number of reviewers increases and is significantly lower compared to average ratings when the number of reviewers is small.
Saleh Hassanzadehyamchi, Amirreza Alizadeh, Ali M. Niknejad, Omeed Momeni
This article proposes a novel approach on cascaded series-connected power amplifier (PA) design. High-frequency transistor modeling is employed to analyze the stacked cell, and a methodology is developed to maximize the output power ($P_\text{out}$) and power-added efficiency (PAE) of each cell. The$P_\text{out}$and power gain of the cell are studied, and the optimum operation point is determined. A proof-of-concept integrated PA is implemented in a 45-nm CMOS silicon-on-insulator (SOI) process, where stacking and parallel power combining techniques are adopted to achieve 18.7-dBm$P_\text{out}$and 4.8% PAE at 200 GHz. Each PA unit uses three cascaded gain stages where two-stacked, three-stacked, and five-stacked architectures are employed for the first, second, and third stages, respectively. Four PA units are power-combined by a low-loss 4:1 zero-degree combiner. The amplifier consumes 1.4-W dc power and has a small-signal gain of 14.6 dB at 203.2 GHz. The designed PA occupies$1.28\times1.05$mm$^\text{2}$die area, including all pads. To the author’s knowledge, the designed PA achieves the highest$P_\text{out}$and PAE among all the Si counterparts at 200 GHz.
Advanced Power Amplifier Design
Radio Frequency Integrated Circuit Design
Electromagnetic Compatibility and Noise Suppression
Zero-knowledge Proof (ZKP) is widely used in applications like online auctions and electronic voting to ensure privacy. Among ZKP algorithms, Zero-Knowledge Succinct NonInteractive Argument of Knowledge (zk-SNARK) stands out for its efficiency in generating concise proofs and reducing verification costs. However, the generation of zk-SNARK proofs poses challenges due to computation overhead and time requirements, hindering practical applications. Multi-Scalar Multiplication (MSM) is a computationally intensive step in zk-SNARK proof generation and has become a focus for industry acceleration efforts. In this paper, we introduce Barrel State Tracking MSM (BSTMSM), a high-performance FPGA-based MSM hardware accelerator. Unlike traditional approaches, BSTMSM focuses on tracking the state of each barrel rather than the pipeline of point addition (PADD) circuits. This approach eliminates the impact of barrel collisions and improves the utilization rate of PADD circuits by enabling the utilization of the associative law of addition. Furthermore, we have successfully implemented up to double PADD circuits in BSTMSM, leading to remarkable performance enhancements compared to other existing works. For an input size of $2^{20}$, BSTMSM outperforms the ASIC-based work PipeZK by $ 1.53\times$. For an input size of $2^{26}$, BSTMSM achieves performance improvements of $ 2.22\times$ compared to the FPGA-based work HARDCAML and $ 1.24\times$ compared to the GPU-based work GZKP.
Decentralized storage platforms based on consortium blockchains have emerged in the spotlight of research and industry communities because they are flexible, transparent, and eliminated trust in contrast to the traditional centralized data-sharing model. However, due to wide attacking surfaces in a blockchain network, this decentralized data-sharing paradigm is subject to malicious data breaches. Untrusted blockchain nodes can directly obtain sensitive information from the query processing and their local storage. Several studies have been made for solving this dilemma, but they only focus on single-user settings and cannot be directly applied to multi-owners blockchain-based data sharing scenarios. In this paper, we introduce FedEDB, a federated and encrypted data store by using consortium blockchains. Unlike existing solutions that focus on single-user settings, our proposed schemes can efficiently support privacy-preserving and reliable multi-owner queries in the decentralized setting. We start from the practical key aggregation technique to construct the multi-owner search schemes and further refine the underling building blocks to enhance the security. Besides, we integrate the smart contract with our tailored zero-knowledge proof to enforce secure and reliable result verification protocol with fairness. We implement a prototype and thorough security analysis and comprehensive evaluation results confirm the practicability of our design.
This paper aims to explore methods and techniques for achieving verifiability in cloud computing environments. Methodologically, by utilizing game theory as the theoretical foundation and employing blockchain and smart contracts as core technologies, we introduce various cooperative mechanisms, including prisoner's contracts, collusion contracts, and traitor contracts, to construct a trustworthy computational framework. In terms of improvements, we innovatively incorporate local blockchains, state channel technology, zero-knowledge proof technology, multi-signature technology, and dynamic reward and punishment mechanisms to additional optimize existing methods. However, the proposed technologies in this paper still require further research and practical validation to assess their actual applicability. Going forward, it is important to enhance blockchain storage and synchronization mechanisms, as well as promote their implementation in broader domains.
Motivated by progress in the field of zero-knowledge proofs, so-called Arithmetization-Oriented (AO) symmetric primitives have started to appear in the literature, such as MiMC, Poseidon or Rescue. Due to the design constraints implied by this setting, these algorithms are defined using simple operations over large (possibly prime) fields. In particular, many rely on simple low-degree monomials for their non-linear layers, essentially using x ↦ x3 as an S-box.In this paper, we show that the structure of the material injected in each round (be it subkeys in a block cipher or round constants in a public permutation) could allow a specific pattern, whereby a well-defined affine space is mapped to another by the round function, and then to another, etc. Such chains of one-dimensional subspaces always exist over 2 rounds, and they can be extended to an arbitrary number of rounds, for any linear layer, provided that the round-constants are well chosen.As a consequence, for several ciphers like Rescue, or a variant of AES with a monomial Sbox, there exist some round-key sequences for which the cipher has an abnormally high differential uniformity, exceeding the size of the Sbox alphabet.Well-known security arguments, in particular based on the wide-trail strategy, have been reused in the AO setting by many designers. Unfortunately, our results show that such a traditional study may not be sufficient to guarantee security. To illustrate this, we present two new primitives (the tweakable block cipher Snare and the permutation-based hash function Stir) that are built using state-of-the-art security arguments, but which are actually deeply flawed. Indeed, the key schedule of Snare ensures the presence of a subspace chain that significantly simplifies an algebraic attack against it, and the round constants of Stir force the presence of a subspace chain aligned with the rate and capacity of the permutation. This in turns implies the existence of many easy-to-find solutions to the so-called CICO problem.
Open access
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Fukang Liu, Mohammad Mahzoun, Morten Øygarden, Willi Meier
Designing novel symmetric-key primitives for advanced protocols like secure multiparty computation (MPC), fully homomorphic encryption (FHE) and zero-knowledge proof systems (ZK), has been an important research topic in recent years. Many such existing primitives adopt quite different design strategies from conventional block ciphers. Notable features include that many of these ciphers are defined over a large finite field, and that a power map is commonly used to construct the nonlinear component due to its efficiency in these applications as well as its strong resistance against the differential and linear cryptanalysis. In this paper, we target the MPC-friendly ciphers AIM and RAIN used for the post-quantum signature schemes AIMer (CCS 2023 and NIST PQC Round 1 Additional Signatures) and Rainier (CCS 2022), respectively. Specifically, we can find equivalent representations of 2-round RAIN and full-round AIM, respectively, which make them vulnerable to either the polynomial method, or the crossbred algorithm, or the fast exhaustive search attack. Consequently, we can break 2-round RAIN with the 128/192/256-bit key in only 2111/2170/2225 bit operations. For full-round AIM with the 128/192/256-bit key, we could break them in 2136.2/2200.7/2265 bit operations, which are equivalent to about 2115/2178/2241 calls of the underlying primitives. In particular, our analysis indicates that AIM does not reach the required security levels by the NIST competition.
Protecting the copyright of large language models (LLMs) has become crucial due to their resource-intensive training and accompanying carefully designed licenses. However, identifying the original base model of an LLM is challenging due to potential parameter alterations. In this study, we introduce HuRef, a human-readable fingerprint for LLMs that uniquely identifies the base model without interfering with training or exposing model parameters to the public. We first observe that the vector direction of LLM parameters remains stable after the model has converged during pretraining, with negligible perturbations through subsequent training steps, including continued pretraining, supervised fine-tuning, and RLHF, which makes it a sufficient condition to identify the base model. The necessity is validated by continuing to train an LLM with an extra term to drive away the model parameters' direction and the model becomes damaged. However, this direction is vulnerable to simple attacks like dimension permutation or matrix rotation, which significantly change it without affecting performance. To address this, leveraging the Transformer structure, we systematically analyze potential attacks and define three invariant terms that identify an LLM's base model. Due to the potential risk of information leakage, we cannot publish invariant terms directly. Instead, we map them to a Gaussian vector using an encoder, then convert it into a natural image using StyleGAN2, and finally publish the image. In our black-box setting, all fingerprinting steps are internally conducted by the LLMs owners. To ensure the published fingerprints are honestly generated, we introduced Zero-Knowledge Proof (ZKP). Experimental results across various LLMs demonstrate the effectiveness of our method. The code is available at https://github.com/LUMIA-Group/HuRef.