Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To address these challenges, we propose a framework that integrates zero-knowledge proof (ZKP) technology, specifically zkVM, with LLM-based chatbots. This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information. Our research introduces both an architecture and a prompting strategy for this approach. Through empirical evaluation, we clarify the current constraints and performance limitations of both zkVM and the proposed prompting strategy, thereby demonstrating their practical feasibility in real-world scenarios.
The rapid increase in Internet of Things (IoT) applications has exposed critical security vulnerabilities, particularly concerning user privacy and identity forgery. To address these concerns, Blockchain technology offers a promising solution by providing strong security and ensuring data integrity through its transparent ledger system. By leveraging blockchain, IoT systems can enhance their security protocols, making it more difficult for attackers to exploit vulnerabilities and access sensitive data. We proposed Attribute-Based Access Control (ABAC) integrated with trust-based delegated consensus blockchain (TDCB) technology. The ABAC scheme employs Fully Homomorphic Encryption (FHE) processes to encrypt attributes and access regulations, enabling analytical operations directly on encrypted data. Dueling Double Deep Q-Networks with Prioritized Experience Replay (D3P) with Deep Reinforcement Learning (DRL) collaborate with Multiple blockchain nodes to decode the ABAC system’s data and optimize the performances of the blockchain. Our proposed scheme ABAC-TDBC-D3P enhances throughput and security and reduces total computing costs. The simulation results demonstrate that the suggested ABAC-TDCB-D3P scheme has a percentage of 86% for Collusive Rumour Attack (CRA) and 91% for Naive Malicious Attack (NMA). Significant improvements in blockchain security, particularly in mitigating the impact of malicious nodes, were compared to previous schemes.
The medical industry has made significant advancements in recent years. However, the lack of accountability in medical management has resulted in systemic deficiencies, which have adversely affected patient trust and contributed to an increase in medical disputes. As a result, there is a growing emphasis on managing the quality of medical services, particularly in enhancing patient experience. To address these challenges, we propose a new system for evaluating health services. This system will allow patients to anonymously rate the services they receive while also providing doctors the opportunity to appeal specific reviews. The hospital handles the evaluations and appeals through the management of the cloud platform. We propose a new scheme to assist the work of the platform, which is a lattice-based group signature with verifier-local revocation (VLR-GS). Most of the work on VLR-GS has focused on the random oracle model (ROM) or using non-interactive zero-knowledge proofs (NIZKs). Our construction is anonymous and traceable in the standard model under the hardness of the learning with errors problem and short integer solution problem. Furthermore, theoretically analyzing it has practical significance in both security and efficiency. In conclusion, the proposed scheme establishes a secure and privacy-oriented platform for an anonymous medical service evaluation system, with the goal of fostering patient trust and improving hospital service quality within the healthcare sector.
Nojan Sheybani, Anees Ahmed, Michel A. Kinsy, Farinaz Koushanfar
Zero-Knowledge Proofs (ZKPs) are a cryptographic primitive that allows a prover to demonstrate knowledge of a secret value to a verifier without revealing anything about the secret itself. ZKPs have shown to be an extremely powerful tool, as evidenced in both industry and academic settings. In recent years, the utilization of user data in practical applications has necessitated the rapid development of privacy-preserving techniques, including ZKPs. This has led to the creation of several robust open-source ZKP frameworks. However, there remains a significant gap in understanding the capabilities and real-world applications of these frameworks. Furthermore, identifying the most suitable frameworks for the developers' specific applications and settings is a challenge, given the variety of options available. The primary goal of our work is to lower the barrier to entry for understanding and building applications with open-source ZKP frameworks. In this work, we survey and evaluate 25 general-purpose, prominent ZKP frameworks. Recognizing that ZKPs have various constructions and underlying arithmetic schemes, our survey aims to provide a comprehensive overview of the ZKP landscape. These systems are assessed based on their usability and performance in SHA-256 and matrix multiplication experiments. Acknowledging that setting up a functional development environment can be challenging for these frameworks, we offer a fully open-source collection of Docker containers. These containers include a working development environment and are accompanied by documented code from our experiments. We conclude our work with a thorough analysis of the practical applications of ZKPs, recommendations for ZKP settings in different application scenarios, and a discussion on the future development of ZKP frameworks.
Performance predictability, ensuring low latency variability, is crucial for the reliability and efficiency of blockchain consensus. Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS) consensus aims to achieve stable transaction processing latency by scheduling block generation at consistent intervals. However, BFT-PoS's incentive mechanisms grant all transaction tips to the block proposer, which can be exploited by delaying proposals to gain extra Maximal Extractable Value (MEV) rewards, thus undermining performance predictability. Existing solutions impose penalties for delays but lack a standard for measuring the extra rewards from delays or fail in malicious environments. This paper introduces InTime, a novel approach to safeguard performance predictability in BFT-PoS by economically motivating timely block proposals. We first introduce the untimely MEV ratio , a reliable metric to measure the extra rewards gained from proposal delays, facilitating our countermeasures against deliberate delays. Furthermore, we propose the arrival rate incentive (ARI), aligning rewards with transaction arrival timing among nodes to reduce potential MEV manipulation. To make ARI robust against malicious behaviors, we establish a committee time witness (CTW) workflow to accurately gather and verify transaction arrival times. Extensive experiments demonstrate that InTime can effectively reduce latency variability by up to 95.9%.
Threshold cryptography is a powerful and well-known technique with many applications to systems relying on distributed trust. It has recently emerged also as a solution to challenges in blockchain: frontrunning prevention, managing wallet keys, and generating randomness. This work presents Thetacrypt, a versatile library for integrating many threshold schemes into one codebase. It offers a way to easily build distributed systems using threshold cryptography and is agnostic to their implementation language. The architecture of Thetacrypt supports diverse protocols uniformly. The library currently includes six cryptographic schemes that span ciphers, signatures, and randomness generation. The library additionally contains a flexible adapter to an underlying networking layer that provides peer-to-peer communication and a total-order broadcast channel; the latter can be implemented by distributed ledgers, for instance. Thetacrypt serves as a controlled testbed for evaluating the performance of multiple threshold-cryptographic schemes under consistent conditions, showing how the traditional micro benchmarking approach neglects the distributed nature of the protocols and its relevance when considering system performance.
This thesis explores the implementation of a Self-Sovereign Identity (SSI) system using Ethereum and Decentralized Identifiers (DIDs). The project focuses on leveraging blockchain technology to create a secure and decentralized framework for digital identity management, incorporating Verifiable Credentials (VCs) and Verifiable Presentations (VPs). Key components include DID document management, secure user authentication, and user-friendly interface. What makes this system different is the integration with existing wallets, privacy and user control through selective disclosure, allowing users to share only necessary information, and key rotation. It also uses EIP-712 signatures for secure and structured data signing, which allows users to clearly see and understand what they are signing while the cryptography is securely handled by the wallet. Future work will focus on adding more wallet support, improving data storage, and enhancing system scalability and security.
Zero-knowledge proofs (ZKPs) are widely applied in digital economies, such as cryptocurrencies and smart contracts, for establishing trust and privacy between untrusted parties. Classical ZKPs rely on computational assumptions and are vulnerable to quantum attacks. While a recent advance suggests quantum-sound symmetric relativistic ZKPs for the graph three-coloring problem without computational assumptions, the high round complexity, which leads to unachievable runtime and overall randomness cost, renders them impractical for real-life deployment. To overcome this, we develop an efficient asymmetric relativistic ZKP protocol using relativistic bit commitments, and prove its quantum soundness by relating it to the nonlocal Clauser-Horne-Shimony-Holt (CHSH) game. Our protocol achieves a linear relationship between the round complexity and the number of edges, and thus significantly improves practical feasibility. In addition, we implement a proof-of-principle experiment which completes all interactive rounds in about 0.22 seconds and requires an overall randomness cost of 430.81 MB. Our work illustrates the powerful potential of integrating special relativity with quantum theory in trustless cryptography, paving the way for robust applications against quantum attacks in distrustful Internet environments. Zero-knowledge proofs can protect privacy online, but almost all current methods are vulnerable to quantum attacks. Here, the authors report an efficient relativistic protocol and experiment that resists quantum attacks and greatly reduces runtime, randomness cost and communication rounds.
Collision-resistant cryptographic hash functions (CRHs) are crucial for security, particularly for message authentication in Zero-knowledge Proof (ZKP) applications. However, traditional CRHs like SHA-2 or SHA-3, while optimized for CPUs, generate large circuits, rendering them inefficient in the ZK domain. Conversely, ZK-friendly hashes are designed for circuit efficiency but struggle on conventional hardware, often orders of magnitude slower than standard hashes due to their reliance on expensive finite field arithmetic. To bridge this performance gap, we present HashEmAll, a novel collection of FPGA-based realizations for three prominent ZK-friendly hashes: Griffin, Rescue-Prime, and Reinforced Concrete. Each offers distinct optimization profiles, with both area-optimized and latency-optimized variants available, allowing users to tailor hardware selection to specific application constraints regarding resource utilization and performance. Our extensive evaluation shows that latency-optimized HashEmAll designs outperform CPU implementations by at least $10 \times$, with the leading design achieving a $23 \times$ speedup. These gains are coupled with lower power consumption and compatibility with accessible FPGAs. Importantly, the highly parallel and pipelined architecture of HashEmAll enables significantly better practical scaling than CPU-based approaches towards building real-world ZKP applications, such as data commitments with Merkle Trees, by mitigating the hashing bottleneck for large trees. This highlights the suitability of HashEmAll for real-world ZKP applications involving large-scale data authentication. We also highlight the ability to translate the HashEmAll methodology to various ZK-friendly hash functions and different field sizes.
Federico Badaloni, Sebastian Holler, Chrysoula Oikonomou, Pedro Moreno-Sánchez · 5 authors
A smart contract is an interactive program that governs funds in the realm of a single cryptocurrency. Yet, the many existing cryptocurrencies have spurred the design of cross-chain applications that require interactions with multiple cryp-tocurrencies simultaneously. Currently, cross-chain applications are implemented as use-case-specific cryptographic protocols that serve as overlay to synchronize smart contract executions in the different cryptocurrencies. Hence, their design requires substantial expertise, as well as a security analysis in complex cryptographic frameworks. In this work, we present$BitML^{x}$, the first domain-specific language for cross-chain smart contracts, enabling interactions with several users that hold funds across multiple Bitcoin-like cryptocurrencies. We contribute a compiler to automatically translate a$BitML^{x}$contract into one contract per involved cryp-tocurrency and a user strategy that synchronizes the execution of these contracts. We prove that an honest user, who follows the prescribed strategy when interacting with the several contracts, ends up with at least as many funds as in the corresponding execution of the$BitML^{x}$contract. Last, but not least, we implement the$BitML^{x}$compiler and demonstrate its utility in the design of illustrative examples of cross-chain applications such as multi-chain donations or loans across different cryptocurrencies.
Open access
3 source records
cs.CR
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Blockchain sharding is a scalable solution for distributed ledgers, but may be hindered due to cross-shard transactions and uneven workload distribution. This paper presents EquiFlowShard, an advanced blockchain sharding protocol designed to improve robustness and enhance cross-shard efficiency. Specifically, by employing Optimized Account State Distribution Algorithm (OSADA), EquiFlowShard dynamically assigns and segments account states, so as to minimize cross-shard transaction volume and balance shard workloads. In addition, the protocol introduces the SFlow mechanism to facilitate secure and consistent state transfers and a Smooth Transition scheme to mitigate performance impacts during state reconfigurations. Evaluation results confirm that EquiFlowShard outperforms existing benchmark protocols in terms of throughput, transaction confirmation latency, and cross-shard transaction ratio, demonstrating its effectiveness in dynamic blockchain environments.
The rapid development of blockchain has significantly promoted research on zero-knowledge proofs (ZKPs), especially zero-knowledge succinct noninteractive arguments of knowledge (zk-SNARK). As is well known, protocol proof and verification time, as well as proof size, are the main obstacles that restrict the implementation of ZKPs in practical applications, so they have become the main concerns of researchers in recent years. This work achieves a new recursive zk-SNARK called GENES, which does not have a trusted setup and is secure under the standard discrete logarithm assumption. GENES is designed from the form of the rank-1 constraint system (R1CS) satisfiability problem. Recursive proof composition is achieved by merging multiple R1CS instances, which transforms the verification of numerous proofs into the verification of a single proof. Moreover, multi-helpers amortize proof commitments in this study, significantly reducing the computational pressure and time cost of proof generation. Compared with previous work, GENES effectively improves the proof time and verification time, but at the cost of larger proof sizes. We provide a blockchain Layer-1 scaling solution leveraging GENES to demonstrate its practicality.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Nowadays, decentralized models connecting various users and entities have gained prominence across the healthcare, finance, and Supply Chain Domains. Decentralized applications represent a transformational approach to data management and transaction execution, emphasizing security, data integrity, and transparency. At the core of these applications lies the blockchain system. This decentralized architecture supports a framework that guarantees data immutability and ensures network-wide transparency through consensus mechanisms. This work aims to explore the application of a blockchain-based system for managing, storing, and signing consent forms within a decentralized framework. By leveraging smart contracts, the system facilitates the creation, modification, deletion, and storage of documents issued by authorized medical entities. Patients can sign these documents, with every alteration and transaction meticulously tracked and recorded, enhancing privacy and data integrity. In addition to these benefits, a private system with role-based access control restricts access to consent forms, as determined by the medical authority that created the documents. The proposed project of this theses aims to leverage these benefits by implementing a Corda application, a blockchain-based solution designed for managing consent forms within the healthcare ecosystem. This solution will enable healthcare providers, patients, and other stakeholders to securely access, share, and manage sensitive medical data with full confidence in its integrity and privacy. By incorporating decentralized technology, the project seeks to create a system where patient consent is stored immutably on the blockchain, ensuring that no unauthorized modifications can be made. Furthermore, the evaluation and testing section of this work reinforces the access security and permission enforcement mechanisms that are proposed and implemented. Rigorous tests and practical examples demonstrate the system's ability to protect patient data and uphold privacy standards, ensuring that only authorized users can interact with sensitive information.
Abstract Authentication and access control for Cyber-Physical Systems (CPSs) are pivotal for protecting systems and their users from problems related to harmful actions and the malicious use of retrieved data. In some situations, making access decisions requires using user information, thereby challenging their privacy. Attribute-based access control (ABAC) supports dynamic and context-aware access decisions that are attractive in cyber-physical system environments. However, privacy preservation for access decisions is an open issue for authorization and is not supported by existing ABAC models. For example, if access decisions need to be made based on private attribute values such as health data, the corresponding access control policies need to be revealed. This paper reviews the ABAC, homomorphic encryption (HE), and zero-knowledge proof (ZKP) approaches, confirming the gap in privacy preservation in ABAC. Based on this observation, we further present the application of a new ZKP-based protocol in which ABAC allows for the privacy-preserving evaluation of attributes. This protocol is implemented and evaluated in terms of its performance and security. The evaluation demonstrates that there is a possibility for privacy-preserving ABAC, which may benefit the use of CPS, e.g., in underground and open-pit mines.
Ahmed M. Tawfik, Ayman Al-Ahwal, Adly S. Tag Eldien, Hala H. Zayed
Abstract Advances in blockchain technology offer a decentralized ledger with transformative potential for healthcare data management, facilitating secure transactions and transparent record-keeping. Nevertheless, the sensitive nature of patient data requires enhanced privacy measures. This paper introduces a comprehensive framework enabling researchers to conduct collaborative statistical analysis on health records while preserving privacy and ensuring security. Statistics are invaluable across various disciplines, guiding consequential decisions based on such analysis. The framework integrates privacy-preserving techniques, including secret-sharing, secure multiparty computation (SMPC), and homomorphic encryption, within a blockchain-based healthcare ecosystem. Patient data is divided using secret-sharing, enabling controlled access. Furthermore, SMPC allows secure data aggregation without revealing individual records, while homomorphic encryption supports computation on encrypted data within smart contracts. Through a series of controlled experiments, we assess the framework’s effectiveness in maintaining data privacy, facilitating secure collaboration, and conducting statistical data analysis. The results demonstrate successful preservation of data privacy and secure analysis on a permissioned blockchain using the Hyperledger Fabric platform. Our framework showcases efficient performance while effectively utilizing system resources. This research contributes to the evolution of secure and privacy-conscious healthcare data analysis, paving the way for practical applications and future advancements.
Federated Learning (FL) has emerged as a promising paradigm that addresses the delicate balance between data-intensive model development and the preservation of user privacy. Unlike the conventional approach of aggregating large volumes of raw data in a single data center, FL conducts local training on various devices or institutional servers—sometimes referred to as “clients”—and only exchanges model parameters or gradients with a central entity. By design, this decentralized framework keeps personal or proprietary data within the confines of the originating device or organization, significantly reducing the chances of exposing sensitive information. A primary motivation for FL is the ever-increasing concern over privacy violations and compliance with stringent regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). As global data protection standards continue to evolve, FL offers a compelling solution by minimizing direct data sharing and thereby mitigating the risk of large-scale breaches. Beyond privacy considerations, FL holds practical appeal in many real-world scenarios, including healthcare, finance, the Internet of Things (IoT), and various consumer-focused applications. These sectors routinely handle confidential or regulated data—medical records, bank transactions, or user habits—where a centralized data repository poses both security and compliance hazards. Nevertheless, FL also introduces its own set of challenges. Heterogeneous data distributions across clients can lead to biases and uneven training dynamics. Additionally, new threat vectors—such as model poisoning and inference attacks—have surfaced within decentralized training environments, prompting research into robust security strategies. Furthermore, practical implementation demands careful planning around communication overhead, computational capacity of clients, and the trade-offs that arise when adding privacy guarantees like Differential Privacy or Secure Multi-Party Computation. This paper explores the theoretical underpinnings of Federated Learning, reviews cutting-edge privacy-preserving techniques, examines potential security pitfalls, and presents real-world applications augmented by case studies. We also discuss performance evaluation methods crucial for determining FL’s viability and highlight upcoming research directions that can shape a secure, efficient, and privacy-centered AI ecosystem.
As an emerging paradigm in digital identity, Decentralized Identity (DID) appears advantages over traditional identity management methods in a variety of aspects, e.g., enhancing user-centric online services and ensuring complete user autonomy and control. Verifiable Credential (VC) techniques are used to facilitate decentralized DID-based access control across multiple entities. However, existing DID schemes generally rely on a distributed public key infrastructure that also causes challenges, such as context information deduction, key exposure, and issuer data leakage. To address the issues above, this paper proposes a issuer-hiding and privacy-preserving DID multi-party authentication model with a signature-less VC scheme, named SLVC-DIDA, for the first time. Our proposed scheme avoids the dependence on signing keys by employing hashing and issuer membership proofs, which supports universal zero-knowledge multi-party DID authentications, eliminating additional technical integrations. We adopt a novel zero-knowledge circuit to maintain the anonymity of the issuer set, thereby enabling public verification while safeguarding the privacy of identity attributes via a Merkle tree-based VC list. Furthermore, by eliminating reliance on a Public Key Infrastructure (PKI), SLVC-DIDA enables decentralized and self-sovereign DID authentication. Our experiments further evaluate the effectiveness and practicality of SLVC-DIDA.
Currently, PAKE (Password Authenticated Key Exchange) protocols on lattice using a single-server architecture are widely applied. However, such protocols are vulnerable to server leakage attacks, dictionary attacks, and other threats. To address these issues, researchers have proposed multi-server and two-server architecture-based PAKE protocols. However, PAKE protocols in a multi-server architecture require the use of complex cryptographic primitives such as signatures, and zero-knowledge proofs to ensure security, which reduces the execution efficiency of the protocol. To solve these problems, we propose a two-server PAKE protocol on the lattice based on the MLWE (Module learning with errors) problem. The protocol is built using instances of the MLWE problem and utilizes the Peikert error coordination technique, which ensures both parties with similar values arrive at the same result through computation. Additionally, we introduce the error pairing hypothesis and demonstrates its security within the random oracle model. The protocol securely stores different shares of password information across various servers. This approach protects user password data, even if one of the servers is compromised. Compared to similar protocols, we avoid the use of numerous cryptographic primitives, and can better resist quantum computing attacks and server leakage. And we reduce computational and communication costs, and can better meet practical application needs.
The emergence of quantum computing has provided new paradigms for cryptography. On the one hand, it poses significant new threats to existing classically cryptographic systems, requiring the community to define new security models that capture what a quantum adversary can do. On the other hand, it gives us new tools to design cryptographic protocols, with weaker assumptions than in the classical world, or even protocols that are impossible classically. In this survey, we first give an overview of new security definitions for classical cryptography, considering quantum adversaries who can either only use local quantum computation (post-quantum security), or even send quantum messages and in particular have access to oracle in superposition (quantum security). We explore these new notions through the examples of commitments, zero-knowledge proofs, encryption, and signatures. Then, we present what is arguably the most famous application of quantum cryptography: quantum key distribution (QKD) protocols that take advantage of unique properties of quantum mechanics to provide secure communication unconditionally. We also explore cryptography beyond QKD, focusing on unclonable cryptography: a family of cryptographic functionalities, built with quantum states, and designed to be resistant to counterfeit by leveraging the “no-cloning” theorem. We examine in particular quantum money, but also the recent notions of unclonable encryption and copy-protection, including related variants. By presenting a comprehensive survey of these topics, this paper aims to provide a thorough understanding of the current landscape and future potential of quantum cryptography.
Abstract— Remote electronic voting promises increased accessibility but remains constrained by persistent challenges related to coercion in unsupervised environments, credential compromise, and the difficulty of sustaining long-term voter trust. While coercion-resistant approaches commonly rely on revoting, most existing systems treat credential loss or recovery as an administrative exception, often reintroducing identity linkage or trusted intermediaries and offering limited means for voters or observers to verify that an election unfolded as intended. This paper presents Arcaunt, a remote voting architecture that elevates anonymous credential recovery to a first-class security property and integrates it directly into the voting lifecycle. The architecture introduces an Anonymous Recovery Channel (ARC), enabling voters to revoke and replace compromised credentials without identity disclosure or reliance on administrator discretion. Recovery is logically and operationally decoupled from ballot casting. This preserves ballot secrecy and prevents temporary compromise of credentials, devices, or voter autonomy from becoming a permanent loss of voting control. Arcaunt builds on established cryptographic mechanisms, including publicly verifiable bulletin boards, commitment-based ballots, and unlinkable bearer credentials. These components provide ballot integrity and verifiability without exposing voter identities and form the foundation on which revoting, recovery, and auditability are composed. Individual assurance is provided through deferred, non-transferable verification mechanisms: voters receive a receipt at ballot submission, while verification becomes possible only after election closure, preventing real-time feedback that could enable coercion while still allowing voters to confirm that their final valid ballot was recorded and included. At the system level, integrity is enforced through an append-only, publicly auditable ledger and deterministic “last valid vote” counting rules, ensuring that administrative database access cannot alter election outcomes without detection. The architecture explicitly bounds its threat model, acknowledging limits against global traffic analysis and continuous coercion while constraining failures to be temporary and non-scalable. We analyze the security properties of the proposed system under realistic adversarial assumptions and evaluate a prototype implementation, demonstrating that anonymous recovery, coercion-resistant revoting, individual verification, and public auditability can be combined efficiently without reliance on trusted administrators or specialized hardware. Keywords—e-voting, arcaunt, anonymous recovery channel (arc), coercion resistance, sha-3, digital democracy, govtech, zero-knowledge proofs.
Folding schemes are an exciting new primitive, transforming the task of performing multiple zero-knowledge proofs of knowledge for a relation into performing just one zero-knowledge proof, for the same relation, and a number of cheap inclusion-proofs. Recently, folding schemes have been used to amortize the cost associated with proving different statements to multiple distinct verifiers, which has various applications. We observe that for these uses, leaking information about the statements folded together can be problematic, yet this happens with previous constructions. Towards resolving this issue, we give a natural definition of privacy preserving folding schemes, and what security they should offer. To construct privacy preserving folding schemes, we first define statement hiders, a primitive which might be of independent interest. In a nutshell, a statement hider hides an instance of a relation as a new instance in the same relation. The new instance is in the relation if and only if the initial instance is. With this building block, we can utilize existing folding schemes to construct a privacy preserving folding scheme, by first hiding each of the statements. Folding schemes allow verifying that a statement was folded into another statement, while statement hiders allow verifying that a statement was hidden as another statement.
With the rapid development of quantum computers, proofs of quantumness have recently become an interesting and intriguing research direction. However, in all current schemes for proofs of quantumness, quantum provers almost invariably face the risk of being maliciously exploited by classical verifiers. In fact, through malicious strategies in interaction with quantum provers, classical verifiers could solve some instances of hard problems that arise from the specific scheme in use. In other words, malicious verifiers can break some schemes (that quantum provers are not aware of) through interaction with quantum provers. All this is due to the lack of formalization that prevents malicious verifiers from extracting useful information in proofs of quantumness. To address this issue, we formalize zero-knowledge proofs of quantumness. Intuitively, the zero-knowledge property necessitates that the information gained by the classical verifier from interactions with the quantum prover should not surpass what can be simulated using a simulated classical prover interacting with the same verifier. As a result, the new zero-knowledge notion can prevent any malicious verifier from exploiting quantum advantage. Interestingly, we find that the classical zero-knowledge proof is sufficient to compile some existing proofs of quantumness schemes into zero-knowledge proofs of quantumness schemes. Due to some technical reason, it appears to be more general to require zero-knowledge proof on the verifier side instead of the prover side. Intuitively, this helps to regulate the verifier's behavior from malicious to be honest-but-curious. As a result, both parties will play not only one role in the proofs of quantumness but also the dual role in the classical zero-knowledge proof. Specifically, the two principle proofs of quantumness schemes: Shor's factoring-based scheme and learning with errors-based scheme in [Brakerski et al, FOCS, 2018], can be transformed into zero-knowledge proofs of quantumness by requiring an extractable non-interactive zero-knowledge argument on the verifier side. Notably, the zero-knowledge proofs of quantumness can be viewed as an enhanced security notion for proofs of quantumness. To prevent malicious verifiers from exploiting the quantum device's capabilities or knowledge, it is advisable to transition existing proofs of quantumness schemes to this framework whenever feasible.
Internal auditing demands innovative and secure solutions in today's business environment, with increasing competitive pressure and frequent occurrences of risky and illegal behaviours. Blockchain along with secure databases like encryption improves internal audit security through immutability and transparency. Hence integrating blockchain with homomorphic encryption and multi-factor authentication improves privacy and mitigates computational overhead. Recently, blockchain applications for internal audits in the enterprise sector are still emerging. Thus, blockchain technology in auditing provides the benefits of enhanced transparency and immutability in data processing, which can establish new solutions for internal auditing but still lacks encryption techniques. The research proposed a framework called "BlockCryptoAudit" to enhance internal audit processes through cryptographic encryption methods and blockchain technology, ensuring secure and transparent audit operations. The proposed approach integrates an additive homomorphic Paillier encryption scheme with blockchain to create a safe and tamper-resident audit trail. Utilizing homomorphic Paillier encryption, BlockCryptoAudit ensures that computations may be performed on encrypted audit data while safeguarding data privacy. The applied blockchain hyperledger component guarantees the immutability and transparency of encrypted audit records, resulting in a decentralized and tamper-resistant record. By limiting data accessibility to authorized individuals based on specified responsibilities, role-based access restrictions handled using smart contracts further strengthen security. The study protects audit data's security and confidentiality by encrypting it and putting it on a blockchain. The study compares the proposed BlockCryptoAudit with models like B-OAP, BSE-DF, and EG-FLB regarding risk mitigation, audit quality, security overhead, and audit trail effectiveness. With little security overhead, BlockCryptoAudit beats out B-OAP, BSE-DF, and EG-FLB in terms of risk mitigation (98%) and audit quality (99%). It is an effective way to improve internal audit processes and guarantee data integrity due to its high performance.
This thesis primarily explores efficient zero-knowledge range arguments as first part and privacy-preserving applications within distributed systems as second part. The first part focuses on zero-knowledge range arguments, a fundamental cryptographic primitive that enables a prover to convince a verifier that a secret value lies within a predefined range without disclosing any unnecessary information. However, deploying range arguments in practice faces significant challenges due to high gas costs and computational overhead. This part contributes to optimizing the {\em verification efficiency} of range arguments to reduce deployment costs on blockchains and other decentralized platforms. We introduce four new zero-knowledge range arguments in the discrete logarithm setting that require only $c \sqrt{\tfrac{N}{\log N}}$ group exponentiations in verification, where $N$ is the number of bits to represent a range and $c$ is a small constant. These improvements make the arguments concretely efficient for blockchain deployment with minimal gas costs. The second part proposes two privacy-enhancing secure distributed systems and applications. First, we introduce a novel paradigm for decentralized privacy-preserving group purchasing for energy plans. Leveraging privacy-preserving blockchain technology and secure multi-party computation, this approach enables users to form coalitions for coordinated switch decisions in a decentralized manner without relying on a trusted third party. We develop an effective solution to support decentralized privacy-preserving group purchasing, which includes a competitive online algorithm for decision-making, secure multi-party computation for enhancing privacy, and zero-knowledge proofs on the blockchain for verifying the private input data used in our online algorithm. Second, we propose a novel scheme zk-qrcode based on anonymous credentials and zk-SNARK. Our scheme leverages the following features: Blockchain-based credential issuance, we eliminate the need for credential issuers to hold signing keys by allowing them to issue credentials to a smart contract on the blockchain; Flexible and composable identity statements, enables users to prove complex statements about their credentials without revealing unnecessary information; QR code based verification: enables user interaction with service providers through QR codes displayed or scanned on mobile phones, incorporating identity proof and access control requests. We implement and evaluate our zk-qrcode scheme in practical use cases for entering bar anonymously. Our results demonstrate that the scheme is efficient and practical, with access control proof generation and verification taking less than 650 milliseconds.