The evolution of decentralized platforms has introduced significant advancements in auction systems; however, these advancements bring forth complex challenges in collateral management within sealed-bid auctions. Traditional approaches, reliant on static collateral, often fail to account for dynamic market conditions and participant behavior, thereby limiting participation from individuals with restricted capital and reducing overall auction efficiency. This research identifies a critical gap in dynamic collateral management for decentralized sealed bid auctions and proposes a novel framework to address these challenges. The proposed method integrates dynamic collateral management within the Riggs-TC (Timed Commitment) protocol, enhancing its existing capabilities to handle collateral more flexibly. By leveraging cryptographic advancements, specifically Pedersen Commitments and Zero-Knowledge Proofs, the framework ensures that collateral adjustments are made in real-time, reflecting each auction phase and participant actions effectively. This method not only secures bid confidentiality but also upholds the integrity and fairness of the auction process. Empirical results from deploying this framework demonstrate a significant reduction in entry barriers for participants, an increase in capital efficiency, and heightened security and fairness across the auction lifecycle. Additionally, the framework's adaptability to various decentralized environments suggests its broad applicative potential beyond the initial case studies.
Decentralized Federated Learning improves data privacy and eliminates single points of failure by removing reliance on centralized storage and model aggregation in distributed computing systems. Ensuring the integrity of computations during local model training is a significant challenge, especially before sharing gradient updates from each local client. Current methods for ensuring computation integrity often involve patching local models to implement cryptographic techniques, such as Zero-Knowledge Proofs. However, this approach becomes highly complex and sometimes impractical for large-scale models that use techniques such as random dropouts to improve training convergence. These random dropouts create non-deterministic behavior, making it challenging to verify model updates under deterministic protocols. We propose ProxyZKP, a novel framework combining Zero-Knowledge Proofs with polynomial proxy models to provide computation integrity in local training to address this issue. Each local node combines a private model for online deep learning applications and a proxy model that mediates decentralized model training by exchanging gradient updates. The multivariate polynomial nature of proxy models facilitates the application of Zero-Knowledge Proofs. These proofs verify the computation integrity of updates from each node without disclosing private data. Experimental results indicate that ProxyZKP significantly reduces computational load. Specifically, ProxyZKP achieves proof generation times that are 30-50% faster compared to established methods like zk-SNARKs and Bulletproofs. This improvement is largely due to the high parallelization potential of the univariate polynomial decomposition approach. Additionally, integrating Differential Privacy into the ProxyZKP framework reduces the risk of Gradient Inversion attacks by adding calibrated noise to the gradients, while maintaining competitive model accuracy. The results demonstrate that ProxyZKP is a scalable and efficient solution for ensuring training integrity in decentralized federated learning environments, particularly in scenarios with frequent model updates and the need for strong model scalability.
Hardware security is the root of trust in all modern ICT (Information and Communications Technology) systems. However, hardware security means something different for different communities. It has also a very wide scope. It covers efficient, secure implementations of new generations of cryptography such as light-weight crypto, post-quantum crypto as well as advanced schemes such as zero-knowledge proofs, fully homomorphic encryption, and computing on encrypted data in general [1][2]. Yet, implementations also must resist a wide variety of side-channel, fault, and micro-architectural attacks. Post-quantum algorithms might resist the attacks developed for quantum computers. Yet, they also have to be resistant to these attacks on classic platforms, see e.g. [3]. Security protocols rely on more than only cryptographic algorithms. They require analog and digital circuit techniques to design quality true random number generators, physically unclonable functions, secure key storage, and many more [4]. A recent report on "Revitalizing the U.S. Semiconductor Ecosystem" (from Executive Office of the President, President's Council of Advisors on Science and Technology, September 2022) [5] describes a set of recommendations on semiconductors and system security. In this presentation, we will demonstrate how our research addresses these recommendations and we will illustrate this with recent results and ongoing projects.
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Physical Unclonable Functions (PUFs) and Hardware Security
To enhance the security of ciphertext-policy attribute-based encryption (CP-ABE) and achieve fully distributed key generation (DKG), this paper proposes a ciphertext access control scheme integrating blockchain and off-chain computation with zero knowledge proof based on Layer-2 and multi-authority CP-ABE. Firstly, we enhance the system into two layers and construct a Layer-2 distributed key management service framework. This framework improves system efficiency and scalability while reducing costs. Secondly, we design the proof of trust contribution (PoTC) consensus algorithm to elect high-trust nodes responsible for DKG and implement an incentive mechanism for key computation through smart contract design. Finally, we design a non-interactive zero-knowledge proof protocol to achieve correctness verification of off-chain key computation. Security analysis and simulation experiments demonstrate that our scheme achieves high security while significantly improving system performance. The time consumption for data users to obtain attribute private keys is controlled at tens of milliseconds.
The increasing popularity of blockchain and cryptocurrencies has led to a considerable proliferation of the cryp-tocurrency market in recent years, pulling more investors into the trillion-dollar industry. The increasing number of users has led to increasing privacy concerns. While the network's ability to facilitate financial inclusion is enhanced by the blockchain's openness and public nature, it also exposes users' transaction histories, raising the possibility of privacy violations and other concerns, such as user profiling. To address these privacy challenges, various techniques emerged, categorized as joint transactions and mixing services. While promising, these methods have limitations, such as potential information leakage and the need for trust in centralized entities. To overcome these shortcomings, we propose a novel system that combines joint transactions and mixing services, incorporating Zero-Knowledge Proofs (ZKPs) for enhanced privacy guarantees. Our proof of concept on the Ethereum blockchain demonstrates improved privacy while providing the basic requirements. The evaluation includes performance metrics and security analysis, focusing on key considerations—unlinkability, verifiability, and double-spending. Our method achieves unlinkability by obfuscating transaction paths through multiple stages (ZKPs, CoinJoin, CoinSwap), ensures verifiability through signatures and commitment exchanges, and prevents double-spending through nonce utilization, all while maintaining a feasible execution time.
Jiawen Fang, Junlang Zhang, Z. G. Yu, Chuming Guan · 5 authors
In order to solve the limitations of externally held accounts (EOA) and contract accounts (CA), Ethereum proposed to combine the two and design an abstract account, which can both initiate transactions and execute code. The Gas payment mechanism of abstract accounts has the risk of user information leakage. In order to solve the problem of user privacy, this paper proposes to use zero-knowledge proof (ZKP) to strengthen user privacy protection, and designs a technical framework for the interaction between Dapps and abstract account wallets; in order to meet the needs of multi-circuit and high efficiency, zero-knowledge proof is combined with BLS technology, and a Gas payment scheme based on proof aggregation and BLS signature (GP-PABLS) is designed; finally, the feasibility and efficiency of the experimental test scheme are designed, which improves the privacy and friendliness of users, provides theoretical and technical guidance for the development of abstract accounts and Gas payment mechanisms, and further promotes the development and improvement of blockchain applications.
In this work, a patient-centric paradigm utilizing IPFS (InterPlanetary File System) storage, blockchain technology, and Zero-Knowledge Proofs (ZKPs) is proposed for handling healthcare data. Traditional healthcare data management systems frequently encounter interoperability, data security, and privacy issues. The technology guarantees safe, decentralized storage and convenient access to medical records by integrating blockchain with IPFS, while ZKPs offer strong privacy protection by permitting key verification without disclosing sensitive information. A framework is proposed that allows healthcare organizations to manage decentralized, tamper-proof healthcare ledgers. Hospitals and doctors are lightweight nodes, although patient nodes may be full or lightweight nodes. The suggested model aims to manage health data through off-chain storage. The technique, which is built on IPFS, protects the blockchain architecture from problems related to scalability. ZKPs also assist in recovering the patient's keys in case they are misplaced or forgotten. With the least amount of effort, the strategy attempts to address and mitigate the patient-centric model's deficits. This method improves efficiency and confidence in healthcare data management while simultaneously giving people ownership over their data. Additionally, the healthcare system is made more resilient, scalable, and safe while protecting patient privacy by integrating blockchain technology with IPFS.
Hafiz Gulfam Ahmad Umar, Mariam Fareed, Sana Rubab, Romiza Rubab
A patient's medical record is an essential tool for treatment and care. However, questions about privacy, interoperability, and data fragmentation have been brought to light by conventional EHR systems. The research here employs blockchain technology to safeguard the transfer and storage of patient data. Since blockchain transactions may be recorded across several computers in a decentralized manner, any future data changes will need updating each subsequent block. The study makes use of tools such as Ganache, Truffle, Metamask, ZKPs, IPFS and RemixIDE. Blockchain validation is used for patient record retrieval; this procedure generates an encrypted Content ID and a decryption symmetric key. Further assurance of privacy and security is provided by the integration of Zero-Knowledge Proofs (ZKPs), which enable users to demonstrate record access privileges without disclosing sensitive information. In addition to boosting resilience and interoperability across systems, this decentralized strategy increases the confidentiality, integrity, transparency, and access management of healthcare data. The combination of blockchain technology with zero-knowledge proofs (ZKPs) increases the reliability of healthcare transactions by safely confirming data access between nodes.
Weihan Li, Zongyang Zhang, Yanpei Guo, Sherman S. M. Chow · 5 authors
Zero-knowledge range proof (ZKRP) asserts that a committed integerVlies in a given range like$[{0, 2^{n}-1}]$without other leakages ofV. It is vital in various privacy-preserving systems. Moving forward, the quest for post-quantum security is still in its infancy; the proof size of state-of-the-art lattice-based ZKRP (Lyubashevsky et al., CCS 20 and Couteau et al., Eurocrypt 21) remains linear inn, directly impacting the long-term sustainability in applications such as immutable ledgers. Confronting this unresolved impasse, we propose SHARP-PQ,i.e., succinct hash-based arbitrary-range proof with post-quantum security. SHARP-PQ offers proof size poly-logarithmic ton, optimized batch proofs, and versatile (new) capabilities. Its success stems from the improved inner product argument and exploitation of homomorphism. Empirically, SHARP-PQ features at least$10\times $smaller proof size for multiple ranges over lattice-based ZKRPs while maintaining competitive prover and verifier times. SHARP-PQ also outperforms ZKRPs directly constructed from hash-based generic zero-knowledge proofs at most$10 \times $.
We show that for every polynomial q* there exist polynomial-size, constant-query, non-adaptive PCPs for NP which are perfect zero knowledge against (adaptive) adversaries making at most q* queries to the proof. In addition, we construct exponential-size constant-query PCPs for NEXP with perfect zero knowledge against any polynomial-time adversary. This improves upon both a recent construction of perfect zero-knowledge PCPs for #P (STOC 2024) and the seminal work of Kilian, Petrank and Tardos (STOC 1997).
Jan Lauinger, Jens Ernstberger, Andreas Finkenzeller, Sebastian Steinhorst
Web users can gather data from secure endpoints and demonstrate the provenance of sensitive data to any third party by using privacy-preserving TLS oracles. In practice, privacy-preserving TLS oracles remain limited and cannot verify larger, sensitive data sets. In this work, we introduce new optimizations for TLS oracles, which enhance the efficiency of selectively verifying the provenance of confidential web data. The novelty of our work is a construction which secures an honest verifier zero-knowledge proof system in the asymmetric privacy setting while retaining security against malicious adversaries. Concerning TLS 1.3 in the one round-trip time (1-RTT) mode, we propose a new, optimized garble-then-prove paradigm in a security setting with malicious adversaries. Our improvements reach new performance benchmarks and facilitate a practical deployment of privacy-preserving TLS oracles in web browsers.
Public Key Encryption with Keyword Search (PEKS) is a widely adopted cryptographic scheme for retrieving encrypted data outsourced to cloud servers based on keywords. However, PEKS is susceptible to both offline and online Keyword Guessing Attacks (KGA). In this paper, We use Auxiliary Server to perform blind signing of user keywords to resist offline KGA and limit online KGA, then we introduce Password-Authenticated Encryption (PHE) as a means of authenticating users performing keyword searches. By leveraging passwords for identity verification and encryption key management, the keyword ciphertexts stored on the Storage Server undergo a secondary encryption process, ensuring that unauthorized users are unable to perform legitimate searches. Current password-based searchable encryption schemes are predominantly symmetric, often resulting in a more complex encryption workflow. Additionally, to address the computational overhead associated with exponentiation and zero-knowledge proofs in PHE-based schemes, we harness the capabilities of Intel SGX, a trusted execution environment. By offloading sensitive computational tasks related to password verification to SGX, we eliminate the need for costly exponentiation and zero-knowledge proofs, thereby enhancing the scheme's performance. This approach also achieves cross-period anonymity and addresses the issue of potential malicious auxiliary servers correlating user requests, as highlighted in Lai et al scheme. Consequently, the novel Server-aided Keyword Search with Password-hardened Encryption (SAKSPHE) we propose provides robust defense against offline KGA, restricts the feasibility of online KGA, and enables password-based user authentication alongside additional encryption of keyword ciphertexts for secure storage.
Attribute-Based Encryption (ABE) is a promising solution for access control in cloud services. However, the heavy decryption overhead hinders its widespread adoption. A general approach to address this issue is to outsource decryption to decryption cloud service(DCS). Existing schemes have utilized various methods to enable users to verify outsourced results; however, they lack an effective mechanism to achieve exemptibility which enables the honest DCS to escape from wrong claims. And it is impractical to assume that the DCS will provide free services. In this paper, we propose a blockchain-based payable outsourced decryption ABE scheme that achieves both verifiability and exemptibility without adding redundant information to ABE ciphertext. We use zero-knowledge proof to verify outsourced results on blockchain and introduce an optional single-round challenge game under optimistic assumption to address the high cost of proof generation. Moreover, our system achieves fairness and decentralized outsourcing to protect the interests of all parties. Finally, we implement and evaluate our scheme on Ethereum to demonstrate its feasibility and efficiency, the gas usage in attribute numbers from 5 to 60 is 11$\times$ to 140$\times$ in the happy case and 4$\times$ to 55$\times$ in the challenge case lower than the scheme of Ge et al. (TDSC'23).
R. Sudharsan, L. Giridharan, S. Anto Micheal Infant, Suresh Annamalai
Voting plays a crucial role in democracy as it is the mechanism through which the will of the people is translated into policies. Ensuring the security, reliability, and privacy of the voting process is necessary to maintain the legitimacy and integrity of the system as a whole. The immutability of voting systems based on blockchain has increased their popularity. Noteworthy advancements in the field of blockchain have been made in the recent decade. It has never been easy to create a secure voting process that preserves fairness and privacy while offering technology systems' adaptability. This research project introduces a method to leverage and integrate a permissioned blockchain, biometric authentication and zero-knowledge proof onto the traditional voting system. The main goal is to enhance the current voting system by making it more secure while ensuring voter's privacy.
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
With the rapid development of Internet of Things (loT) technology, the vast amount of data generated by its devices has raised widespread concern for user privacy pro-tection. Differential Privacy, as a stringent privacy protection measure, plays a significant role in safeguarding individual data. However, implementing Differential Privacy in the loT environment faces challenges in ensuring data utility. This paper focuses on proposing a Verifiable Differential Privacy (VDP) scheme based on zero-knowledge proofs, under the premise of ensuring data utility. The scheme requires data publishing entities to provide publicly verifiable proofs to confirm the reliability of the dataset and the effectiveness of privacy protection. By introducing a commitment mechanism and range proofs, our model not only protects the interests of data users but also enhances trust in the enforcement of privacy protection measures. The experimental results show that the scheme can effectively filter out unreasonable Differential Privacy noise, ensuring the privacy and reliability of data, offering a new approach to data privacy protection in the loT field.
Koustav Kumar Mondal, Ashi Gupta, Debasis Das, Chun‐I Fan
This research introduces PISTON, a novel protocol designed to enhance the security, efficiency, and performance of Internet of Vehicles (IoV) networks. PISTON integrates advanced authentication mechanisms utilizing Physically Unclonable Functions (PUFs) and multifactor authentication with dynamic challenges and zero-knowledge proof-based authentication to ensure robust security and mitigate various cyber threats, including Denial-of-Service (DoS) attacks. The protocol further incorporates sleep-wake scheduling, priority-based scheduling, and adaptive modulation and coding to optimize network performance. The communication overhead in PISTON is derived through a formula that incorporates latency, energy consumption, and throughput, demonstrating the protocol’s efficiency in dynamic vehicular environments. Comparative analysis against existing protocols highlights PISTON’s superiority in seamless handover, provable security, and DoS attack resilience. Experimental results show that PISTON reduces energy consumption by 30% and achieves 20% higher data throughput while maintaining low latency, essential for real-time IoV applications. The empirical findings underscore PISTON’s advancements in establishing a new benchmark for future IoV deployments, ensuring secure, energy-efficient, low-latency, and high-throughput communication.
Abstract On‐chain sealed auctions represent a novel approach to electronic bidding auctions, wherein the introduction of zero‐knowledge proof technology has significantly enhanced the security of auctions. However, most mainstream on‐chain sealed auction schemes currently employ Bulletproofs to prove auction correctness, which leaves room for optimization in terms of verification time and inherent security. Addressing these issues, an on‐chain sealed auction scheme based on zero‐knowledge succinct non‐interactive argument of knowledge (zk‐STARK) is proposed. This scheme leverages the decentralization and immutability of blockchain and smart contracts to eliminate third‐party involvement while ensuring the security of the auction process. The Inter Planetary File System is utilized to provide a qualification review mechanism for the auctioneer, enabling the screening of unqualified bidders before the auction. Additionally, the scheme employs RSA encryption to conceal bidders' bids, Pedersen commitments to ensure the consistency of bidding information, and zk‐STARKs to verify the correctness of the winning bid. Security analysis and experimental results demonstrate that the proposed scheme meets the required security standards, with time consumption at various stages of the auction being within acceptable limits, and effectively reduces the time required for proof verification.
Blockchain technologies and paradigms can provide new ground for the management of complex socio-technical problems like supply chains. The contribution of this work is a work-in-progress experimental setting to demonstrate the effectiveness of a novel distributed business process management scheme that integrates the RMAS (Relational-model Multi Agent System) and the Blockchain frameworks. The research work is conducted in a food supply chain case study aiming to sustainability and social good through a purposeful organisation of the autonomous behaviour of the actors in the system.