Decentralized Identifiers have recently expanded into Internet of Things devices and are crucial in securing users' digital identities and data. However, Decentralized Identifiers face challenges in scenarios necessitating authority delegation and anonymity, such as when dealing with legal guardianship for minors, device loss or damage, and specific medical contexts involving patient information. This paper aims to strengthen data sovereignty within the Decentralized Identifier system by implementing a secure authority delegation and anonymity scheme. It suggests optimizing verifiable presentations by utilizing a sequential aggregate signature, a Non-Interactive Zero-Knowledge Proof, and a Merkle tree to prevent against linkage and Sybil attacks while facilitating delegation. This strategy mitigates security risks related to delegation and anonymity, efficiently reduces the computational and verification efforts for signatures, and reduces the size of verifiable presentations by about 1.2 to 2 times.
Secure and efficient communication between vehicles is a prerequisite for providing intelligent services in the Internet of Vehicles (IoV). The openness and high mobility of the IoV communication environment impose stringent requirements on privacy and efficiency. Hence, we propose an efficient hybrid anonymous message authentication scheme for the IoV environment. The scheme is based on utilizing discrete logarithm zero-knowledge proofs and combines global pseudo identity for communication between network entities and local pseudo identity for communication within a domain, aiming to achieve efficient and authenticated secure communication among vehicles with conditional privacy protection. Additionally, through the design of a verification tuple, the message receiver can authenticate the sender's identity upon receiving the message, addressing security vulnerabilities arising from the inability of traditional schemes to promptly revoke malicious vehicles. The performance analysis indicates that the scheme, in comparison to other similar solutions, successfully mitigates the drawback of high time expenditure in traditional message authentication schemes, demonstrating its efficiency and applicability, meeting the security and efficiency requirements of communication in the IoV environment. The feasibility analysis reveals that the total delay introduced by the scheme is significantly lower than the communication duration, meeting the requirements of high-speed dynamic scenarios in the Io V.
In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants.
To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obscure the true identities of blockchain transaction participants. By integrating commitment schemes and zero-knowledge proof techniques with deep learning graph neural network technology, it provides privacy protection and regulatory analysis of blockchain transaction data. This approach not only prevents the leakage of sensitive transaction information, but also achieves regulatory capabilities at both macro and micro levels, ensuring the verification of the legality of transactions. By adopting an identity-based encryption system, regulatory bodies can conduct personalized supervision of blockchain transactions without storing users’ actual identities and key data, significantly reducing storage computation and key management burdens. Our scheme is independent of any particular consensus mechanism and can be applied to current blockchain technologies. Simulation experiments and complexity analysis demonstrate the practicality of the scheme.
S. B. Goyal, Anand Singh Rajawat, Manoj Kumar, Prerna Agarwal
INTRODUCTION: Cloud computing's offshoot, fog computing, moves crucial data storage, processing, and networking capabilities closer to the people who need them. There are certain advantages, such improved efficiency and lower latency, but there are also some major privacy and security concerns. For these reasons, this article presents a new paradigm for fog computing that makes use of blockchain and Artificial Intelligence (AI). OBJECTIVES: The main goal of this research is to create and assess a thorough framework for fog computing that incorporates AI and blockchain technology. With an emphasis on protecting the privacy and integrity of data transactions and streamlining the management of massive amounts of data, this project seeks to improve the security and privacy of Industrial Internet of Things (IIoT) systems that are cloud-based. METHODS: Social network analysis methods are utilised in this study. The efficiency and accuracy of data processing in fog computing are guaranteed by the application of artificial intelligence, most especially Support Vector Machine (SVM), due to its resilience in classification and regression tasks. The network's security and reliability are enhanced by incorporating blockchain technology, which creates a decentralised system that is tamper resistant. To make users' data more private, zero-knowledge proof techniques are used to confirm ownership of data without actually disclosing it. RESULTS: When applied to fog computing data, the suggested approach achieves a remarkable classification accuracy of 99.8 percent. While the consensus decision-making process of the blockchain guarantees trustworthy and secure operations, the support vector machine (SVM) efficiently handles massive data analyses. Even in delicate situations, the zero-knowledge proof techniques manage to keep data private. When these technologies are integrated into the fog computing ecosystem, the chances of data breaches and illegal access are greatly reduced. CONCLUSION: Fog computing, which combines AI with blockchain, offers a powerful answer to the privacy and security issues with cloud centric IIoT systems. Combining SVM with AI makes data processing more efficient, while blockchain's decentralised and immutable properties make it a strong security measure. Additional security for user privacy is provided via zero-knowledge proofs. Improving the privacy and security of fog computing networks has never been easier than with this novel method.
Number theoretic transform (NTT) is a fundamental building block in emerging cryptographic constructions such as fully homomorphic encryption (FHE), post-quantum cryptography (PQC), and zero-knowledge proof (ZKP). In this work, we introduce Proteus, an open-source parametric hardware to generate pipelined architectures for the NTT. For a given parameter set including the polynomial degree and size of the coefficient modulus, Proteus can generate Radix-2 NTT architectures using single-path delay feedback (SDF) and multipath delay commutator (MDC) approaches. We also present a detailed analysis of NTT implementation approaches and use several optimizations to achieve the best NTT configuration. Our evaluations demonstrate performance gain up to$1.8\times$compared to SDF and MDC-based NTT implementations in the literature. Our SDF and MDC architectures use$1.75\times$and$6.5\times$less DSPs, and$3\times$and$10.5\times$less BRAMs, respectively, compared to state-of-the-art SDF and MDC-based NTT implementations.
Zero-knowledge proof (ZKP) systems have surged attention and held a fundamental role in contemporary cryptography. Zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) protocols dominate the ZKP usage, implemented through arithmetic circuit programming paradigm. However, underconstrained or overconstrained circuits may lead to bugs. The former refers to circuits that lack the necessary constraints, resulting in unexpected solutions and causing the verifier to accept a bogus witness, and the latter refers to circuits that are constrained excessively, resulting in lacking necessary solutions and causing the verifier to accept no witness. This paper introduces a novel approach for pinpointing two distinct types of bugs in ZKP circuits. The method involves encoding the arithmetic circuit constraints to polynomial equation systems and solving them over finite fields by the computer algebra system. The classification of verification results is refined, greatly enhancing the expressive power of the system. A tool, AC4, is proposed to represent the implementation of the method. Experiments show that AC4 demonstrates a increase in the solved rate, showing a 29% improvement over Picus and CIVER, and a slight improvement over halo2-analyzer, a checker for halo2 circuits. Within a solvable range, the checking time has also exhibited noticeable improvement, demonstrating a magnitude increase compared to previous efforts.
Mohammed Alghazwi, Dewi Davies-Batista, Dimka Karastoyanova, Fatih Türkmen
Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healthcare, utilize these statistics in advancing research and improving patient care. In this work, we explore the challenge of input validation and public verifiability within privacy-preserving aggregation protocols. We address the scenario in which a party receives data from multiple sources and must verify the validity of the input and correctness of the computations over this data to third parties, such as auditors, while ensuring input data privacy. To achieve this, we propose the "VPAS" protocol, which satisfies these requirements. Our protocol utilizes homomorphic encryption for data privacy, and employs Zero-Knowledge Proofs (ZKP) and a blockchain system for input validation and public verifiability. We constructed VPAS by extending existing verifiable encryption schemes into secure protocols that enable N clients to encrypt, aggregate, and subsequently release the final result to a collector in a verifiable manner. We implemented and experimentally evaluated VPAS with regard to encryption costs, proof generation, and verification. The findings indicate that the overhead associated with verifiability in our protocol is 10x lower than that incurred by simply using conventional zkSNARKs. This enhanced efficiency makes it feasible to apply input validation with public verifiability across a wider range of applications or use cases that can tolerate moderate computational overhead associated with proof generation.
Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generating images with unseen characteristics beneficial for downstream tasks has received limited attention. To bridge this gap, we propose Style-Extracting Diffusion Models, featuring two conditioning mechanisms. Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e.g., layout for segmentation. We introduce a trainable style encoder to extract style information from images, and an aggregation block that merges style information from multiple style inputs. This architecture enables the generation of images with unseen styles in a zero-shot manner, by leveraging styles from unseen images, resulting in more diverse generations. In this work, we use the image layout as target condition and first show the capability of our method on a natural image dataset as a proof-of-concept. We further demonstrate its versatility in histopathology, where we combine prior knowledge about tissue composition and unannotated data to create diverse synthetic images with known layouts. This allows us to generate additional synthetic data to train a segmentation network in a semi-supervised fashion. We verify the added value of the generated images by showing improved segmentation results and lower performance variability between patients when synthetic images are included during segmentation training. Our code will be made publicly available at [LINK].
The explosive growth of Large-Language Models (LLMs), particularly Generative Pre-trained Transformer (GPT) models, has revolutionised fields ranging from natural language processing to creative writing. Yet, their reliance on vast, often unverified data sources introduces a critical vulnerability: unreliability and security concerns. Traditional GPT models, while impressive in their capabilities, struggle with limited factual accuracy and susceptibility to manipulation by biased or malicious data. This poses a significant risk in professional and personal environments where sensitive or mission-critical data is paramount. This work tackles this challenge head-on by proposing a novel approach to enhance GPT security and reliability: leveraging Zero-Knowledge Proofs (ZKPs). Unlike traditional cryptographic methods that require sensitive data exchange, ZKPs allow one party to convincingly prove the truth of a statement, without revealing the underlying information. In the context of GPTs, ZKPs can validate the legitimacy and quality of data sources used in GPT computations, combating data manipulation and misinformation. This ensures trustworthy outputs, even when incorporating third-party data (TPD). ZKPs can securely verify user identities and access privileges, preventing unauthorised access to sensitive data and functionality. This protects critical information and promotes responsible LLM usage. ZKPs can identify and filter out manipulative prompts designed to elicit harmful or biased responses from GPTs. This safeguards against malicious actors and promotes ethical LLM development. ZKPs facilitate training specialised GPT models on targeted datasets, resulting in deeper understanding and more accurate outputs within specific domains. This allows the creation of ‘expert-GPT’ applications in specialised fields like healthcare, finance, and legal services. The integration of ZKPs into GPT models represents a crucial step towards overcoming trust and security barriers. Our research demonstrates the viability and efficacy of this approach, with our ZKP-based authentication system achieving promising results in data verification, user control, and malicious prompt detection. These findings lay the groundwork for a future where GPTs, empowered by ZKPs, operate with unwavering integrity, fostering trust and accelerating ethical AI development across diverse domains.
In an era marked by technological advancements and a growing demand for secure and transparent electoral processes, the integration of blockchain technology into online voting systems has emerged as a promising solution.This research paper presents a comprehensive exploration of the design, implementation, and implications of an online voting system built upon blockchain technology.Through an in-depth analysis of existing electronic voting challenges and the potential of blockchain, this paper demonstrates how the decentralized, immutable, and transparent nature of blockchain addresses critical concerns such as security, voter privacy, and trust in electoral outcomes.The paper delves into the core architecture of the proposed system, highlighting the role of smart contracts in automating voting processes while ensuring authenticity and verifiability.Security and transparency are examined in detail, showcasing the cryptographic measures that safeguard voter information and prevent fraudulent activities.The challenges of voter authentication, scalability, and accessibility are discussed, along with potential solutions to overcome these obstacles.Drawing on case studies of real-world implementations, the paper offers insights into the successes, challenges, and lessons learned from adopting blockchain-based online voting systems.Legal and ethical considerations are also explored, emphasizing the need for aligning technological innovations with legal frameworks and ethical standards.Finally, the research paper contemplates the future of blockchain-powered online voting, envisioning how emerging technologies such as biometrics, artificial intelligence, and zero-knowledge proofs could further enhance the security and inclusivity of electoral processes.Overall, this paper underscores the transformative potential of blockchain in revolutionizing online voting, fostering a more resilient and democratic electoral landscape.
Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.
Xi Lin, Heyang Cao, Feng-Hao Liu, Zhedong Wang · 5 authors
Abstract Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are cryptographic protocols that offer efficient and privacy-preserving means of verifying NP language relations and have drawn considerable attention for their appealing applications, e.g., verifiable computation and anonymous payment protocol. Compared with the pre-quantum case, the practicability of this primitive in the post-quantum setting is still unsatisfactory, especially for the space complexity. To tackle this issue, this work seeks to enhance the efficiency and compactness of lattice-based zk-SNARKs, including proof length and common reference string (CRS) length. In this paper, we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic rings. Compared with previous works, our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK schemes. Particularly, the proof length of our scheme is around $$23.3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>23.3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al. (in: Proceedings of the 2021 ACM SIGSAC conference on computer and communications security, pp 212–234, 2021), and the CRS length is $$3.6\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3.6</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> smaller. Our constructions follow the framework of Gennaro et al. (in: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, pp 556–573, 2018), and adapt it to the ring setting by slightly modifying the knowledge assumptions. We develop concretely small constructions by using module-switching and key-switching procedures in a novel way.
Lixing Chen, Feng Gao, Yang Bai, Jun Wu · 6 authors
Blockchain has revolutionized a variety of fields by providing decentralization, immutability, transparency, and auditability. This paper designs Blockchained Edge Resource Auction (BERA) for edge computing systems to allocate computing resources to application service providers (ASP) in a secure manner. BERA comprises two key components: Blockchain-based Sealed-Bid Auction (BSBA) and Graph Neural Network (GNN)-based Fraud Detection (GFD). BSBA designs smart contracts to realize sealed-bid auctions overhead blockchain. It incorporates the homomorphic commitment technique to guarantee the transactional privacy of ASPs’ bidding information and performs interval membership zero-knowledge proof to verify the legitimacy of auction results. While the privacy-preserving property of BSBA is desirable, the veiled bidding information tends to breed fraudulent behaviors. Therefore, GFD is further proposed to identify abnormal auction behaviors in BSBA without revealing bidding information of ASPs. GFD converts the blockchain data of BSBA to an auction behavioral graph of ASPs, and uses GNN to discover stealth frauds based on interactive patterns. In addition, we design a subgraph extraction scheme for GFD to improve its scalability. We implement BERA on a private Ethereum blockchain and successfully realize edge resource auctions. We simulate several types of auction frauds and identify them with GFD. The experimental results show that our method outperforms other benchmarks.
Purpose The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. Design/methodology/approach This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. Findings This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. Originality/value This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.
We construct perfect zero-knowledge probabilistically checkable proofs (PZK-PCPs) for every language in #P. This is the first construction of a PZK-PCP for any language outside BPP. Furthermore, unlike previous constructions of (statistical) zero-knowledge PCPs, our construction simultaneously achieves non-adaptivity and zero knowledge against arbitrary (adaptive) polynomial-time malicious verifiers. Our construction consists of a novel masked sumcheck PCP, which uses the combinatorial nullstellen- satz to obtain antisymmetric structure within the hypercube and randomness outside of it. To prove zero knowledge, we introduce the notion of locally simulatable encodings: randomised encodings in which every local view of the encoding can be efficiently sampled given a local view of the message. We show that the code arising from the sumcheck protocol (the Reed–Muller code augmented with subcube sums) admits a locally simulatable encoding. This reduces the algebraic problem of simulating our masked sumcheck to a combinatorial property of antisymmetric functions.
The scalability limitations of public blockchains have hindered their widespread adoption in real-world applications. While the Ethereum community is pushing forward in zk-rollup (zero-knowledge rollup) solutions, such as introducing the ``blob transaction'' in EIP-4844, Layer 2 networks encounter a data availability problem: storing transactions completely off-chain poses a risk of data loss, particularly when Layer 2 nodes are untrusted. Additionally, building Layer 2 blocks requires significant computational power, compromising the decentralization aspect of Layer 2 networks. This paper introduces new techniques to address the data availability and decentralization challenges in Layer 2 networks. To ensure data availability, we introduce the concept of ``proof of download'', which ensures that Layer 2 nodes cannot aggregate transactions without downloading historical data. Additionally, we design a ``proof of storage'' scheme that punishes nodes who maliciously delete historical data. For decentralization, we introduce a new role separation for Layer 2, allowing nodes with limited hardware to participate. To further avoid collusion among Layer 2 nodes, we design a ``proof of luck'' scheme, which also provides robust protection against maximal extractable value (MEV) attacks. Experimental results show our techniques not only ensure data availability but also improve overall network efficiency, which implies the practicality and potential of our techniques for real-world implementation.
This paper proposes a novel hybrid method to increase trust and privacy in the Onion Router (Tor) network by integrating blockchain technology and Zero Knowledge Proofs (ZKPs). Leveraging principles from trust-based anonymous communication, the proposed method aims to establish a decentralized trust layer within the Tor network, enhancing integrity and reliability. By incorporating ZKPs’ power of privacy and authentication, blockchain's immutability and Tor's anonymity, the method seeks to address trust and privacy concerns within the Tor network. This hybrid approach offers a comprehensive solution to increase trust and privacy in the Tor network, aligning with their growing importance in the digital age.
Internet Traffic Analysis and Secure E-voting
Network Security and Intrusion Detection
Advanced Steganography and Watermarking Techniques
Khushi Garg, Mahee Jattu, S. Hariharasitaraman, R. Raja Subramanian
The emergence of Web 3.0 has paved the way for a new generation of the internet that enables users to view, publish, and own content independently. This innovation is being driven by a combination of blockchain, semantic communication, edge computing, and artificial intelligence, which together create value networks that facilitate participatory decision-making. Blockchain, in particular, offers security services by recording content in a decentralized and transparent manner. Additionally, it is compatible with decentralized wireless edge computing designs that can accurately express the intended meanings of contents without requiring a lot of resources. Building on this foundation, this research proposes a paradigm for unified blockchain-semantic ecosystems for Web 3.0 with wireless edge intelligence. Specifically, we present a semantic proof technique built on Oracle that enables Web 3.0's off-chain and on-chain interactions. To improve interaction efficiency and support Web 3.0, Oracle has developed an adaptable Deep Reinforcement Learning-based sharding technique. Our work also introduces a blockchain-based semantic exchange architecture that tokenizes semantic data into non-fungible tokens (NFT) for semantic exchange in this system. We employ Zero-Knowledge Proof to trade real semantic information without publicly posting it before accepting payment, in contrast to traditional NFT marketplaces. This makes it possible to trade fairly and privately. Overall, our proposed framework can precisely perform decentralized semantic sharing and information transfer, further achieving the advantages of semantic extraction and communication in Web 3.0.
Healthcare Insurance Industries have revolutionized storing patient information in digital health records(EHRs) enabling to access data more efficiently and effectively. Using a blockchain insurance plan helps to grow a business that would be transparent and immutable. This project is to create an Ethereum smart contract insurance that will self-execute with a computer program with the terms and conditions and can set the accessible data and an individual amount as well as health insurance amount by their specialized doctors which is all written into code and stored on the Ethereum blockchain and will only be executed when there will be clear verification conveyed by the verifier and that is the ultimate goal of this research paper. In this scenario, Zero Knowledge proof has been introduced which has the potential to provide a clear solution from the prover to verify without interfering or revealing essential information about the patient or the process. And the reason for using Zk-SNARKs is for the Zcash algorithm which provides full-fledged transactions with strong privacy. In this paper, we elaborate on Ethereum smart contracts with the Zcash algorithm for healthcare insurance to computational cost using the Zk-SNARKs scheme.
Priced oblivious transfer (POT) is a cryptographic protocol designed for privacy-preserving e-commerce of digital content. It involves two parties: the merchant, who provides a set of priced items as input, and a customer, who acquires one of them. After the protocol has run, the customer obtains the item they chose, while the merchant cannot determine which one. Moreover, the protocol guarantees that the customer gets the content only if they have paid the price established by the merchant. In a recent paper, the authors proposed a POT system where the payments employed e-coin transactions. The strong point of the proposal was the absence of zero-knowledge proofs required in preceding systems to guarantee the correctness of payments. In this paper, we propose a novel e-coin-based POT system with a fast item retrieval procedure whose running time does not depend on the number of items for sale. This is an improvement over the aforementioned existing proposal whose execution time becomes prohibitively long when the catalog is extensive. The use of zero-knowledge proofs is neither required.