Authentication is a crucial security service on the Internet. In real-world applications, multiple independent trust domains often exist, with each recognizing only certain identities within their own systems. During cross-domain access, users cannot directly use their original certificates, which presents a cross-domain authentication problem. Traditional centralized schemes typically employ a trusted third party (TTP) to facilitate the transfer of identity trust across domains. These schemes inevitably inherit the vulnerabilities associated with single points of failure. In contrast, blockchain-based decentralized schemes effectively eliminate the potential threats posed by TTPs. However, the openness and transparency of the blockchain also bring new security issues, such as privacy leakage. In this paper, we propose a zk-SNARK-based anonymous scheme on the blockchain for cross-domain authentication. Specifically, our scheme adopts an authorization-then-proof structure, which strikes a delicate balance between anonymity and revocability. We provide theoretical proofs for the security of our scheme and explain how it achieves proactive revocability. Experimental evaluation results demonstrated that our scheme is both secure and efficient, and the revocation could be accomplished by introducing only 64 bytes of on-chain storage with one hash comparison.
In the field of cloud computing, ensuring secure and efficient key agreement among multiple parties has emerged as a paramount challenge. Traditional key agreement protocols often rely on central authorities or trusted third parties, posing significant security and privacy concerns. To address these challenges, this paper introduces a novel key agreement protocol designed specifically for cloud computing platforms, emphasizing security, efficiency, and resilience without depending on a trusted third party. The proposed protocol innovatively combines Distributed Key Generation (DKG) with a Dynamic Consensus Mechanism, Zero-Knowledge Proof (ZKP) based authentication, and a Multi-Cloud Redundancy approach, offering a comprehensive solution to secure multi-party communication in distributed cloud environments. The DKG protocol facilitates the collaborative generation of a shared secret among participants, significantly enhancing security by eliminating single points of failure. The proposed Dynamic Consensus Mechanism ensures the integrity and finality of key agreement transactions on a blockchain-based ledger, adapting to network conditions and participant trust levels to optimize performance without compromising security. ZKP-based authentication allows participants to verify their identities without revealing sensitive information, preserving privacy and thwarting impersonation attacks. Lastly, the Multi-Cloud Redundancy strategy enhances the protocol's resilience to cloud-specific vulnerabilities and service outages, ensuring high availability and robustness.
AI data sharing platforms must reconcile two pressures that often clash: the need to exchange highâvalue datasets for model development and evaluation, and the obligation to guarantee privacy, integrity, and verifiability of computations on that data. This manuscript surveys and synthesizes cryptographic building blocksâdifferential privacy, homomorphic encryption, multiparty computation with secure aggregation, zeroâknowledge proofs, attribute-based encryption and proxy re-encryption, trusted execution environments, and domain standards such as Crypt4GHâinto a pragmatic, layered architecture for AI data sharing. We outline a methodology that integrates policy-aware access control with threshold key management, private training and inference, verifiable analytics, and auditability. A compact statistical analysis (with an illustrative table) demonstrates how such a stack can bound leakage (Îľ), preserve utility (accuracy), and manage computational overhead (latency). Results show that a hybrid PETs (privacy-enhancing technologies) approachâcombining local differential privacy and secure aggregation for ingestion, homomorphic encryption or TEEs for computation, and zk-proofs for verifiabilityâachieves strong privacy with modest accuracy loss and acceptable latency for many enterprise scenarios. We conclude with design guidelines and research directions for standards-aligned, future-ready AI data sharing platforms.
Sagar Dev Achar, P Thejaswini, Sukumar Nandi, Sunit Kumar Nandi
The global paradigm shift toward edge computing has led to a growing demand for efficient integrity verification. Hash functions are one-way algorithms which act as a zero-knowledge proof of a datumâs contents. However, it is infeasible to compute hashes on devices with limited processing power and memory. Hence, we propose four novel LiteHash functions which are architecturally similar to SHA-512 yet simpler. By using various approximation techniques, our implementations reduce the computational costs of digesting a message into a hash. On validating our proposed designs using the NIST PRNG Test Suite, we observe SHA-512 equivalent cryptographic security while satisfying all desired hash function property requirements. We observe a minimum of 9.41% reduction in area, 20.47% reduction in power, and 22.05% increase in throughput. Our designs offer a throughput of up to 2 Gbps while reducing area and power by a maximum of 16.86% and 32.48%, respectively. LiteHash functions also support the computation of the entire SHA-2 family of hash functions (SHA-224/256/384/512) with minor architectural modifications.
Physical Unclonable Functions (PUFs) and Hardware Security
D. Dhinakaran, D. Selvaraj, N. Dharini, S. Edwin Raja ¡ 5 authors
The intersection of cloud computing, blockchain technology, and the impending era of quantum computing presents a critical juncture for data security. This research addresses the escalating vulnerabilities by proposing a comprehensive framework that integrates Quantum Key Distribution (QKD), CRYSTALS Kyber, and Zero-Knowledge Proofs (ZKPs) for securing data in cloud-based blockchain systems. The primary objective is to fortify data against quantum threats through the implementation of QKD, a quantum-safe cryptographic protocol. We leverage the lattice-based cryptographic mechanism, CRYSTALS Kyber, known for its resilience against quantum attacks. Additionally, ZKPs are introduced to enhance data privacy and verification processes within the cloud and blockchain environment. A significant focus of this research is the performance evaluation of the proposed framework. Rigorous analyses encompass encryption and decryption processes, quantum key generation rates, and overall system efficiency. Practical implications are scrutinized, considering factors such as file size, response time, and computational overhead. The evaluation sheds light on the framework's viability in real-world cloud environments, emphasizing its efficiency in mitigating quantum threats. The findings contribute a robust quantum-safe and ZKP-integrated security framework tailored for cloud-based blockchain storage. By addressing critical gaps in theoretical advancements, this research offers practical insights for organizations seeking to secure their data against quantum threats. The framework's efficiency and scalability underscore its practical feasibility, serving as a guide for implementing enhanced data security in the evolving landscape of quantum computing and blockchain integration within cloud environments.
To be useful and widely accepted, automated contact tracing schemes (also called exposure notification) need to solve two seemingly contradictory problems at the same time: they need to protect the anonymity of honest users while also preventing malicious users from creating false alarms. In this paper, we provide, for the first time, an exposure notification construction that guarantees the same levels of privacy and integrity as existing schemes but with a fully malicious database (notably similar to Auerbach et al. CT-RSA 2021) without special restrictions on the adversary. We construct a new definition so that we can formally prove our construction secure. Our definition ensures the following integrity guarantees: no malicious user can cause exposure warnings in two locations at the same time and that any uploaded exposure notifications must be recent and not previously uploaded. Our construction is efficient, requiring only a single message to be broadcast at contact time no matter how many recipients are nearby. To notify contacts of potential infection, an infected user uploads data with size linear in the number of notifications, similar to other schemes. Linear upload complexity is not trivial with our assumptions and guarantees (a naive scheme would be quadratic). This linear complexity is achieved with a new primitive: zero knowledge subset proofs over commitments which is used by our "no cloning" proof protocol. We also introduce another new primitive: set commitments on equivalence classes, which makes each step of our construction more efficient. Both of these new primitives are of independent interest.
M. Albrecht, Matilda Backendal, Daniele Coppola, Kenneth G. Paterson
Nextcloud is a leading cloud storage platform with more than 20 million users. Nextcloud offers an end-to-end encryption (E2EE) feature that is claimed to be able âto keep extremely sensitive data fully secure even in case of a full server breachâ. They also claim that the Nextcloud server âhas Zero Knowledge, that is, never has access to any of the data or keys in unencrypted formâ. This is achieved by having encryption and decryption operations that are done using file keys that are only available to Nextcloud clients, with those file keys being protected by a key hierarchy that ultimately relies on long passphrases known exclusively to the users. We provide the first detailed documentation and security analysis of Nextcloud's E2EE feature. Nextcloud's strong security claims motivate conducting the analysis in the setting where the server itself is considered malicious. We present three distinct attacks against the E2EE security guarantees in this setting. Each one enables the confidentiality and integrity of all user files to be compromised. All three attacks are fully practical and we have built proof-of-concept implementations for each. The vulnerabilities make it trivial for a malicious Nextcloud server to access and manipulate users' data. We have responsibly disclosed the three vulnerabilities to N extcloud. The second and third vulnerabilities have been remediated. The first was addressed by temporarily disabling file sharing from the E2EE feature until a redesign of the feature can be made. We reflect on broader lessons that can be learned for designers of E2EE systems.
Elena Andreeva, Rishiraj Bhattacharyya, Arnab Roy, Stefano Trevisani
ZK-SNARKs, a fundamental component of privacyoriented payment systems, identity protocols, or anonymous voting systems, are advanced cryptographic protocols for verifiable computation: modern SNARKs allow to encode the invariants of a program, expressed as an arithmetic circuit, in an appropriate constraint language from which short, zero-knowledge proofs for correct computations can be constructed. One of the most important computations that is run through SNARK systems is the verification of Merkle tree (MT) opening proofs, which relies on the evaluation of a fixed-input-length (FIL) cryptographic compression function over binary MTs. As classical, bit-oriented hash functions like SHA-2 are not compactly representable in SNARK frameworks, Arithmetization-Oriented (AO) cryptographic designs have emerged as an alternative, efficient solution. Today, the majority of AO compression functions are built from permutation-based hashing modes, such as Sponge. While this approach allows cost savings, compared to blockcipher-based modes, as it does not require key-scheduling, AO blockcipher schedulers are often cheap to compute. Furthermore, classical bitoriented cryptography has long studied how to construct provably secure compression functions from blockciphers, following the Preneel-Govaerts-Vandewalle (PGV) framework. The potential efficiency gains together with the strong provable security foundations in the classic setting, motivate the study of AO blockcipher-based compression functions. In this work, we propose AO PGV-LC and PGV-ELC, two AO blockcipher-based FIL compression modes inspired by and extending the classical PGV approach, offering flexible input and output sizes and coming with provable security guarantees in the AO setting. We prove the collision and preimage resistance in the ideal cipher model, and give bounds for collision and opening resistance over MTs of arbitrary arity. We compare experimentally the AO PGV-ELC mode over the HADES blockcipher with its popular and widely adopted Sponge instantiation, POSEIDON, and its improved variant POSEIDON2. Our resulting constructions are up to 3Ă faster than POSEIDONAND 2Ă faster than POSEIDON2 in native x86 execution, and up to 50% faster in the Groth16 SNARK framework. Finally, we study the benefits of using MTs of arity wider than two, proposing a new strategy to obtain a compact R1CS constraint system in such case. In fact, by combining an efficient parametrization of the HADES blockcipher over the PGV-ELC mode, together with an optimal choice of the MT arity, we measured an improvement of up to 9Ă in native MT construction time, and up to 2.5Ă in proof generation time, compared to POSEIDON over binary MTs.
Behzad Abdolmaleki, Noemi Glaeser, Sebastian Ramacher, Daniel Slamanig
Non-interactive zero-knowledge proofs (NIZKs) and in particular succinct NIZK arguments of knowledge (zk-SNARKs) increasingly see real-world adoption in large and complex systems. Many zk-SNARKs require a trusted setup, i.e., a common reference string (CRS), and for practical use it is desirable to reduce the trust in the CRS generation. The latter can be achieved via the notions of subversion or updatable CRS. Another important property when deployed in large systems is the ability to securely compose them to obtain more complex protocols, e.g., via the Universal Composability (UC) framework. Relying on the UC framework allows arbitrary and secure composition of protocols in a modular way. In this work, we investigate whether zk-SNARKs can provide updatability and composability simultaneously. This is a challenging task as the UC framework rules out several natural techniques for such a construction. As our main result, we show that it is indeed possible to achieve these properties in a generic and modular way if we relax the succinctness properties of zk-SNARKs slightly to those of a circuit-succinct NIZK which is not witness-succinct, i.e., by increasing the proof size of the underlying zk-SNARK by the size of the witness$w$. We argue that for various practical applications of zk-SNARKs this overhead is acceptable. Our starting point is the Lamassu framework (ACM CCS'20), which we extend in several directions. Our new generic compiler adds only minimal overhead, which we demonstrate by benchmarking its application to the Sonic proof system (ACM CCS'19).
Farhana Javed, Josep ManguesâBafalluy, Engin Zeydan, Luis Blanco
In the domain of Collaborative Artificial Intelligence, Federated Learning ($\mathbf{F L}$) is a technique that enables multiple entities to collaboratively refine AI models while adhering to stringent data privacy standards, without the need for direct data sharing. This paper explores the integration of blockchain technology with FL to establish reliable trust mechanisms within this collaborative framework. We highlight and review current blockchain-enabled reputation mechanisms that evaluate the reliability and quality of contributions from participants, which are crucial for maintaining trust and operational integrity in distributed settings. Through our review, we address the concept and implementation challenges. Additionally, we discuss recent technological advances and explore the emerging opportunities that blockchain presents to address trust-related challenges in FL, emphasizing significant prospects for future research directions, such as decentralized identities, zero trust, and zero-knowledge proofs to enhance trust in these environments.
This paper presents MQ on my Mind (MQOM), a digital signature scheme based on the difficulty of solving multivariate systems of quadratic equations (MQ problem). MQOM has been submitted to the NIST call for additional post-quantum signature schemes. MQOM relies on the MPC-in-the-Head (MPCitH) paradigm to build a zero-knowledge proof of knowledge (ZK-PoK) for MQ which is then turned into a signature scheme through the Fiat-Shamir heuristic. The underlying MQ problem is non-structured in the sense that the system of quadratic equations defining an instance is drawn uniformly at random. This is one of the hardest and most studied problems from multivariate cryptogra-phy which hence constitutes a conservative choice to build candidate post-quantum cryptosystems. For the efficient application of the MPCitH paradigm, we design a specific MPC protocol to verify the solution of an MQ instance. Compared to other multivariate signature schemes based on non-structured MQ instances, MQOM achieves the shortest signatures (6.3-7.8 KB) while keeping very short public keys (few dozen of bytes). Other multivariate signature schemes are based on structured MQ problems (less conservative) which either have large public keys (e.g UOV) or use recently proposed variants of these MQ problems (e.g. MAYO).
Fischlin's transform (CRYPTO 2005) is an alternative to the Fiat-Shamir transform that enables straight-line extraction when proving knowledge. In this work we focus on the problem of using the Fischlin transform to construct UC-secure zero-knowledge from Sigma protocols, since UC security â that guarantees security under general concurrent composition â requires straight-line (non-rewinding) simulators. We provide a slightly simplified transform that is much easier to understand, and present algorithmic and implementation optimizations that significantly improve the running time. It appears that the main obstacles to the use of Fischlin in practice is its computational cost and implementation complexity (with multiple parameters that need to be chosen). We provide clear guidelines and a simple methodology for choosing parameters, and show that with our optimizations the running-time is far lower than expected. For just one example, on a 2023 MacBook, the cost of proving the knowledge of discrete log with Fischlin is only 0.41ms (on a single core). This is 15 times slower than plain Fiat-Shamir on the same machine, which is a significant multiple but objectively not significant in many applications. We also extend the transform so that it can be applied to batch proofs, and show how this can be much more efficient than individually proving each statement. We hope that this paper will both encourage and help practitioners implement the Fischlin transform where relevant.
Blockchainâs decentralization, transparency, and tamperâresistance are celebrated properties for auditability and trust, yet they collide with core data protection duties under the EU General Data Protection Regulation (GDPR). This manuscript analyzes the principal compliance challenges that arise when blockchain processes personal data and proposes a practical, design-oriented framework to address them. First, we synthesize legal and regulatory positions on what counts as âpersonal data,â the difference between anonymization and pseudonymization, and the implications of the right to erasure, data protection by design and by default, allocation of controller/processor roles, and international data transfers. We then map these requirements to blockchain architectures (public permissionless, public permissioned, and private permissioned) and data patterns (on-chain, off-chain, hybrid). Building on recent guidance from the European Data Protection Board (EDPB) and national authorities, we outline concrete technical and governance controlsâoff-chain storage and on-chain commitments, keyed hashing, encryption/key-revocation strategies, chameleon-hash/redactable-ledger designs, selective-disclosure credentials/zero-knowledge proofs, and robust consortium governanceâto reduce risk and improve demonstrable compliance. Applying a six-step assessment methodology to three realistic use cases (NFT profile registry, supply-chain provenance, and consortium KYC), we show that while no single pattern fully reconciles immutability with erasure, practicable combinations can align processing with GDPRâs principles of minimization, purpose limitation, storage limitation, and accountability. The paper concludes with a prioritized checklist for engineering âcompliance-by-designâ blockchains, and delineates scope and limitations for practitioners and researchers.
This paper presents new techniques for private billing in systems for privacy-preserving online advertising. In particular, we show how an ad exchange can use an e-cash scheme to bill advertisers for ad impressions without learning which client saw which ad: The exchange issues electronic coins to advertisers, advertisers pay publishers (via clients) for ad impressions, and publishers unlinkably redeem coins with the exchange. To implement this proposal, we design a new divisible e-cash scheme that uses modern zero-knowledge proofs to reduce the ad exchange's computational costs by roughly 250x compared to the previous state-of-the-art. With our new e-cash scheme, our private-billing infrastructure adds little overhead to existing private ad-retargeting systems: less than 63 ms of latency, negligible client computation, less than 3.2 KB of client communication, and a combined server operating cost (advertisers, publishers, and exchange) of less than 1% of ad spend, an over 5x savings compared to the previous state-of-the-art.
WebAssembly, or WASM for short, is a binary code format for a stack-based virtual machine, first published in 2018 and now becomes a main-steam technology for providing distributed serverless functions. Recently, the demand for privacy and trustless serverless functions has started to grow in cloud, edge, and grid computing, which poses a question for those serverless function providers: how they ensure trustworthy computation in safety-critical scenarios like financial systems, cybersecurity, private data handling, etc. To address this, we leverage the technology ZKSNARK (zero-knowledge Succinct Non-interactive Argument of Knowledge), a powerful proof system that allows efficient verification of the evaluation problem of statements, to give WASM runtime the ability to provide trustless computation service. More precisely, we present ZKWASM, a ZKSNARK backed virtual machine that emulates the execution of WASM bytecode and generates zero-knowledge-proofs for the emulation result. The proof generated by the ZKWASM virtual machine can then be used to convince an entity, with no leakage of confidential information, that the result of the emulation enforces the semantic specification of WASM.
Artificial intelligence (AI) and distributed ledger technologies are increasingly integrated into public and private surveillance infrastructuresâfrom city-wide camera networks to critical-infrastructure monitoring and access control. This integration promises higher integrity and accountability through immutable logs, faster incident response via on-device inference, and interoperable audit trails across organizations. Yet it also amplifies ethical risks: mass data collection, opacity in model decisions, function creep, demographic harms, cross-border data governance conflicts, and accountability gaps when immutable records meet âright to erasureâ regimes. This manuscript proposes an ethics-by-design reference architecture for blockchain-powered surveillance that embeds privacy, proportionality, and fairness controls into each lifecycle stage (purpose definition â data capture â model training â inference â access â audit â decommissioning). Technically, it composes privacy-enhancing technologies (PETs)âincluding differential privacy, federated learning, zero-knowledge proofs, verifiable credentials (VCs), and content-provenance standards (C2PA)âwith permissioned blockchain ledgers, model cards, and risk management aligned to the NIST AI RMF, ISO/IEC 23894, ISO/IEC 42001, UNESCO, and ACM guidance. A simulated evaluation illustrates how the architecture can reduce false-positive disparities and unauthorized access, while preserving evidentiary integrity. We discuss tensions with GDPR (e.g., Article 17 erasure; DPIA obligations), constraints introduced by the EU AI Act (e.g., prohibitions and high-risk biometric uses), and strategies to reconcile immutability with privacy (e.g., off-chain storage with revocation, redaction-friendly commitments). The paper closes with limitations and a future research agenda for measurable, auditable ethical guarantees in real-time surveillance.
This study builds on our previous systematic literature review (SLR) that assessed the applications and performance of zk-SNARK, zk-STARK, and Bulletproof non-interactive zero-knowledge proof (NIZKP) protocols. To address the identified research gaps, we designed and implemented a benchmark comparing these three protocols using a dynamic minimized multiplicative complexity (MiMC) hash application. We evaluated performance across four general-purpose programming libraries and two programming languages. Our results show that zk-SNARK produced the smallest proofs, while zk-STARK generated the largest. In terms of proof generation and verification times, zk-STARK was the fastest, and Bulletproof was the slowest. Interestingly, zk-SNARK proofs verified marginally faster than zk-STARK, contrary to other findings. These insights enhance our understanding of the functionality, security, and performance of NIZKP protocols, providing valuable guidance for selecting the most suitable protocol for specific applications.
Intention adoption represents a migration from an imperative to a declarative paradigm, which is expected to significantly improve the user experience in blockchain. While the development of account abstraction has expanded the range of possibilities for intent expression, the architecture for intent-centric collaboration and coordination remains underdeveloped. Existing works depend on trusted centralized entities and employ broadcasts to the network to disseminate user intent, risking single points of failure and privacy leakage. In addition, no standard intent architecture has been proposed. In this paper, we propose a novel decentralized privacy-preserving optimizer architecture for executing intents on the EVM blockchain. We first propose standard intent structures and zero-knowledge intent structures. Our architecture allows users to transform raw intent structures into zero-knowledge intent structures and send them to a set of trusted optimizers. The optimizers can optimize and execute the intents without revealing the content of the intents. We use zero-knowledge proof and public-key encryption to ensure the privacy and correctness of the intents, and smart contracts to ensure the security and verifiability of the execution. We also design a quantization function to evaluate and compare solutions from different optimizers. We analyze the security and performance of our architecture and show that it can achieve privacy, efficiency, and scalability of intent execution on the EVM blockchain.
Decentralized applications (Dapps), based on smart contract technology, have been increasingly applied in various fields such as healthcare, industrial IoT, agriculture, financial services, supply chain management, and insurance. In certain complex business scenarios, blockchain may require machine learning models to assist contract business. However, on-chain computations are often costly and slow, and there are limitations on contract size. Due to the transparency of on-chain data, there are also privacy concerns regarding user data during model training and inference. To address these challenges, we propose a trusted off-chain machine learning solution that integrates ZK-SNARK and Oracle technologies. Following the principle of âoff-chain computation, on-chain verificationâ,our approach leverages ZK-SNARK to delegate the computation tasks of machine learning models to a trusted environment under the Oracle off-chain server. This solution significantly reduces the computational costs of the blockchain. User data and models are executed off-chain, effectively safeguarding user privacy. The execution results generate zero-knowledge proofs returned for on-chain verification. We have implemented TOMLS-ZKSO and conducted relevant experiments on insurance contract business on the Chainmaker. Experimental results demonstrate the effectiveness of our approach, with model proof generation taking approximately 0.4 seconds and Dapp response time around 0.52 seconds.
A. Bendada, Mouhamed Amine Bouchiha, Mourad Rabah, Yacine Ghamri-Doudane
Current blockchain-based reputation solutions for crowdsourcing fail to tackle the challenge of ensuring both efficiency and privacy without compromising the scalability of the block chain. Developing an effective, transparent, and privacy-preserving reputation model necessitates on-chain implementation using smart contracts. However, managing task evaluation and reputation updates alongside crowdsourcing transactions on-chain substantially strains system scalability and performance. This paper introduces RollupTheCrowd, a novel blockchain-powered crowdsourcing framework that leverages zkRollups to enhance system scalability while protecting user privacy. Our framework includes an effective and privacy-preserving reputation model that gauges workers' trustworthiness by assessing their crowdsourcing interactions. To alleviate the load on our blockchain, we employ an off-chain storage scheme, optimizing RollupTheCrowd's performance. Utilizing smart contracts and zero-knowledge proofs, our Rollup layer achieves a significant 20x reduction in gas consumption. To prove the feasibility of the proposed framework, we developed a proof-of-concept implementation using cutting-edge tools. The experimental results presented in this paper demonstrate the effectiveness and scalability of RollupTheCrowd, validating its potential for real-world application scenarios.
In this article, we propose zero-knowledge named proof, a stateless replay attack prevention strategy that ensures the userâs anonymity against malicious administrators. We begin with adopting the zero-knowledge set-membership proof into an authentication setting in which users would delegate their requests to an agent that obstructs the userâs identity from the administrator. This anonymous agent carries the guarantee of authenticity, which the administrator through the set-membership proof can confirm. Next, we prevent replay attacks from other parties by binding the agentâs identity to the authentication proof verifiable by the administrators. By leveraging these properties, a scalable blockchain-based authentication scheme is then built. We quantitatively evaluate the security and measure the time and monetary cost of our scheme under both ideal and realistic environments. On top of it, we provide a third-party authorization scheme derived from our authentication framework to demonstrate its real-world applicability.
As cryptographic technologies evolve, the need for specialized hash functions to operate efficiently over different computational environments becomes necessary. Traditional symmetric algorithms like AES and SHA-3 have been optimized for traditional hardware and software implementations, which are designed over binary fields. However, protocols like zero-knowledge proofs require hash functions that are optimised over large prime fields. This thesis addresses the growing demand for Arithmetization-Oriented (AO) cryptographic hash functions for zero-knowledge applications. The performance and efficiency of many zero-knowledge applications often depends on the efficiency of the hash function used. In response to this need, this work explores a selection of these hash functions and implements them within two zero-knowledge proving systems: Dusk Network?s Plonk and Polygon?s Plonky2, with a focus on assessing the different performance tradeoffs that these hash functions offer within these sytems.
With the development of Internet of Things technology, intelligent door lock devices are widely used in the field of house leasing. In the traditional housing leasing scenario, problems of door lock information disclosure, tenant privacy disclosure and rental contract disputes frequently occur, and the security, fairness and auditability of the housing leasing transaction cannot be guaranteed. To solve the above problems, a blockchain-based proxy re-encryption scheme with conditional privacy protection and auditability is proposed. The scheme implements fine-grained access control of door lock data based on attribute encryption technology with policy hiding, and uses proxy re-encryption technology to achieve auditable supervision of door lock information transactions. Homomorphic encryption technology and zero-knowledge proof technology are introduced to ensure the confidentiality of housing rent information and the fairness of rent payment. To construct a decentralized housing lease transaction architecture, the scheme realizes the efficient collaboration between the door lock data ciphertext stored under the chain and the key information ciphertext on the chain based on the blockchain and InterPlanetary File System. Finally, the security proof and computing performance analysis of the proposed scheme are carried out. The results show that the scheme can resist the chosen plaintext attack and has low computational cost.
Current digital payment solutions are fragile and offer less privacy than traditional cash. Their critical dependency on an online service used to perform and validate transactions makes them void if this service is unreachable. Moreover, no transaction can be executed during server malfunctions or power outages. Due to climate change, the likelihood of extreme weather increases. As extreme weather is a major cause of power outages, the frequency of power outages is expected to increase. The lack of privacy is an inherent result of their account-based design or the use of a public ledger. The critical dependency and lack of privacy can be resolved with a Central Bank Digital Currency that can be used offline. This thesis proposes a design and a first implementation for an offline-first digital euro. The protocol offers complete privacy during transactions using zero-knowledge proofs. Furthermore, transactions can be executed offline without third parties and retroactive double-spending detection is facilitated. To protect the users' privacy, but also guard against money laundering, we have added the following privacy-guarding mechanism. The bank and trusted third parties for law enforcement must collaborate to decrypt transactions, revealing the digital pseudonym used in the transaction. Importantly, the transaction can be decrypted without decrypting prior transactions attached to the digital euro. The protocol has a working initial implementation showcasing its usability and demonstrating functionality.