This project focuses on Zero-Knowledge Proofs (ZKPs), a groundbreaking cryptographic technique reshaping data authentication while preserving maximum confidentiality.ZKPs enable the verification of truthfulness in statements without disclosing associated data, ensuring the utmost protection of sensitive information.With applications spanning various domains, including secure authentication protocols, privacy-preserving transactions in decentralized systems like blockchain, and confidential data verification across digital interactions, ZKPs offer versatile solutions for secure communications.The project aims to safeguard sensitive business information during outsourcing service processes.The implementation of ZKPs intends to establish a secure communication framework that fosters trust among stakeholders without compromising sensitive details, ensuring enhanced confidentiality in outsourced operations.At its core, ZKPs empower a prover to convince a verifier of a statement's validity without revealing underlying data, establishing an unmatched level of security and privacy.This concept shields against unauthorized access and data breaches, fostering trust between entities without the exchange of sensitive details.The versatility of ZKPs extends beyond authentication, influencing secure voting systems, safeguarding digital identities, and facilitating confidential transactions while upholding user privacy.
Whilst the blessing of the cloud, which provides for adaptability and easy data sharing as well as storage, comes with some security doubts. We place a major responsibility on our backs of protecting the data provided by our users from any evil attempts to sneak in and also risks of being accidentally exposed and the ones that exist through shared infrastructure. This paper traverses this complex terrain by noting the need for targeted security solutions which are aimed towards dealing with the compounded nature of the fast-growing problem. By exploring such threats as data breaches, the possible encryption breaks and the nature of server sharing we will analyze the safety of cloud-based services. We position that the existing security methods, though very necessary, are not exclusive in responding to the ones posed by the new threats. This means we will therefore be rather flexible in our design of the data security in the cloud. By the way, we will employ innovative strategies, maybe, by replacing a word for words such as homomorphic encryption, zero-knowledge proofs, and federated learning, presenting how it holds promise for private and confidential assets. Furthermore, we investigate the exploding influence of blockchain technology, regarding the wages it might deserve in providing manipulated data authentication and creating trust. The work in this paper creates a path towards a future when database users' virtual information in the cloud is safe and certified. Through proposing a wide-ranged approach, which equates realized ideas with well-established security frameworks, we shall lead a cloud infrastructure where stability and power would prevail. Keywords – Cloud Computing, Security Issues, Security Challenges
The current healthcare ecosystem often faces challenges such as data breaches, unauthorized access, data silos, and difficulties in tracking and verifying medical device supply chains. These issues pose a threat to patient confidentiality, trust in healthcare data, and the efficiency of healthcare operations. To address these challenges, a proposed model introduces a unique approach to bolstering the security and efficiency of Internet of Things (IoT) devices in healthcare through the use of advanced cryptographic techniques. Specifically, the model utilizes a heavy computation algorithm known as Elliptic Curve Cryptography (ECC) for packet verification in IoT devices. In addition to ECC, the model incorporates Zero Knowledge Proof (ZKP) for verification, providing a lighter alternative to ECC. ZKP works by signing the input messages and subsequently verifying the data, offering a robust security measure. The implementation strategy involves simulating IoT sensors, each equipped with ECC and Zero Knowledge proof capabilities for enhanced security.
Succinct Non-Interactive Zero-knowledge Arguments of Knowledge or SNARKs are important cryptographic primitives that allow a party to generate a certificate of the knowledge of some secret without leaking anything about the secret. The last decade has seen a massive surge in the research efforts centered around SNARKs leading to very efficient protocols that are now implemented in several real world applications. Because of their prevalence in practice it is important for SNARKs to have good security guarantees along with being efficient. Strengthening security guarantees of SNARKs is the central theme of this thesis.<br/><br/>The usual way of proving security for SNARKs is by proving game-based definitions such as knowledge soundness and non-interactive zero-knowledge. However knowledge soundness typically guarantees security only against a corrupt prover working in isolation. This is at odds with how SNARKs are used in the real world where there are several provers generating and exchanging proofs with each other, for example, on the blockchain. In these situations a prover can potentially launch malleability attacks where it can prove knowledge of a secret without actually knowing the associated secret. To rule out malleability attacks, we need a stronger property than knowledge soundness known as simulation-extractability (SIM-EXT). But in many cases, not even simulation-extractability is sufficient---for instance when we are running other cryptographic protocols in parallel, or using the SNARK as a sub-protocol. These are all examples of protocol composition where, to guarantee security in general, we need the SNARKs to be universally composable (UC-secure).<br/><br/> In this thesis we go beyond knowledge soundness and study SIM-EXT and UC security in the context of SNARKs. As our first contribution, we study a popular SNARK system called Bulletproofs, and prove that the non-interactive version of Bulletproofs via Fiat-Shamir transform is non-malleable. The result here relies on the Algebraic Group Model (AGM) and the Random Oracle Model (RO). Our next contribution is towards generalizing and simplifying our approach in order to capture non-malleability of a larger classes of SNARKs in one shot. For this we look at SNARKs built from polynomial interactive oracle proofs and compiled using polynomial commitments. We identify simple to verify properties from these two building blocks that are sufficient to claim non-malleability for the compiled SNARK (without having to look at the protocol details of the SNARK). As our final contribution we focus on UC-secure SNARKs. We provide a new compiler in the global random oracle model that lifts a non-malleable SNARK to a UC secure one while preserving succinctness. An important consequence of this result is the first constant proof size UC-secure SNARK in the global random oracle model.
S. Jegadeesan, R. K. Hayvita, Ahamed Nishath, S. Ramalakshmi · 6 authors
Data synchronization is a critical aspect in the deployment of Digital Twins within Fog-Edge Cloud environments, ensuring consistency and reliability across distributed systems. However, the security and efficiency of synchronization techniques remain significant challenges. The paper presents a novel algorithm named TwinCrypt, which is designed based on the combination of appropriate encryption algorithm called as Homomorphic Encryption and suitable computing mechanism scheme called as Secure Multi-Party Computation. The algorithm is aimed to achieve secure and efficient data synchronization for Digital Twins in Fog-Edge Cloud environments. The proposed algorithm aims to address vulnerabilities associated with data transmission and storage, offering robust protection against unauthorized access and data breaches. The performance effectiveness of TwinCrypt is analysed by conducting suitable simulations and the simulation results are compared with are conducted comparing it with the existing algorithms used for data synchronization viz. RSA Encryption, AES Encryption, and Zero-Knowledge Proofs in similar environments. Simulation metrics such as latency, throughput, and security overhead are employed to assess the performance of TwinCrypt against its counterparts. The results establish that TwinCrypt outperforms existing algorithms in both security and efficiency, showcasing its suitability for real- world deployment scenarios.
Zero-knowledge proof is a cryptographic primitive that allows for the validation of statements without disclosing any sensitive information, foundational in applications like verifiable outsourcing and digital currency. However, the extensive proof generation time limits its widespread adoption. Even with GPU acceleration, proof generation can still take minutes, with Multi-Scalar Multiplication (MSM) accounting for about 78.2% of the workload. To address this, we present DistMSM, a novel MSM algorithm tailored for distributed multi-GPU systems. At the algorithmic level, DistMSM adapts Pippenger's algorithm for multi-GPU setups, effectively identifying and addressing bottlenecks that emerge during scaling. At the GPU kernel level, DistMSM introduces an elliptic curve arithmetic kernel tailored for contemporary GPU architectures. It optimizes register pressure with two innovative techniques and leverages tensor cores for specific big integer multiplications. Compared to state-of-the-art MSM implementations, DistMSM offers an average 6.39× speedup across various elliptic curves and GPU counts. An MSM task that previously took seconds on a single GPU can now be completed in mere tens of milliseconds. It showcases the substantial potential and efficiency of distributed multi-GPU systems in ZKP acceleration.
Haochen Sun, J. Li, Change Institutions to: University of Waterloo
The recent surge in artificial intelligence (AI), characterized by the prominence of large language models (LLMs), has ushered in fundamental transformations across the globe. However, alongside these advancements, concerns surrounding the legitimacy of LLMs have grown, posing legal challenges to their extensive applications. Compounding these concerns, the parameters of LLMs are often treated as intellectual property, restricting direct investigations. In this study, we address a fundamental challenge within the realm of AI legislation: the need to establish the authenticity of outputs generated by LLMs. To tackle this issue, we present zkLLM, which stands as the inaugural specialized zero-knowledge proof tailored for LLMs to the best of our knowledge. Addressing the persistent challenge of non-arithmetic operations in deep learning, we introduce tlookup, a parallelized lookup argument designed for non-arithmetic tensor operations in deep learning, offering a solution with no asymptotic overhead. Furthermore, leveraging the foundation of tlookup, we introduce zkAttn, a specialized zero-knowledge proof crafted for the attention mechanism, carefully balancing considerations of running time, memory usage, and accuracy. Empowered by our fully parallelized CUDA implementation, zkLLM emerges as a significant stride towards achieving efficient zero-knowledge verifiable computations over LLMs. Remarkably, for LLMs boasting 13 billion parameters, our approach enables the generation of a correctness proof for the entire inference process in under 15 minutes. The resulting proof, compactly sized at less than 200 kB, is designed to uphold the privacy of the model parameters, ensuring no inadvertent information leakage.
B.P. Sreeja, G Rajeshkumar, Alagu Kiruthika B, Sasi Prabha N · 6 authors
Distributed storage, which is one of the main functions of distributed computing, enables clients of cloud services to think of their data as being stored in the cloud and to share it with qualified customers. In distributed storage, stable deduplication has been widely researched because it can eliminate overt repetitiveness within scrambled data to reduce storage space significantly. In the field of safety and protection, many current continuous deduplication schemes highlight to a large extent the associated characteristics: information classification, label consistency, access control animal attack assaults. One technique for ensuring the accuracy of information in garage outsourcing is confirmed information possession. The article discusses the development of a green PDP scheme for a distributed cloud garage to facilitate provider migration scalability and statistics, where we keep in mind the lifecycle of multiple cloud service providers to maintain and maintain collaborative customer statistics. The cooperative scheme relies entirely on verifiable homomorphic reactions. Completeness, knowledge soundness, and zero-knowledge qualities may all be satisfied by our multi-proof zero-knowledge proof system, which strengthens the security of our system. The experiment shows that this solution has lower computational and communication overhead than non-cooperative approaches.
We introduce Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to third parties that they are within a specified geographical region while not disclosing their exact location. ZKLP supports varying levels of granularity, allowing for customization depending on the use case. To realize ZKLP, we introduce the first set of Zero-Knowledge Proof (ZKP) circuits that are fully compliant to the IEEE 754 standard for floating-point arithmetic. Our results demonstrate that our floating point circuits amortize efficiently, requiring only $64$ constraints per multiplication for $2^{15}$ single-precision floating-point multiplications. We utilize our floating point implementation to realize the ZKLP paradigm. In comparison to a baseline, we find that our optimized implementation has $15.9 \times$ less constraints utilizing single precision floating-point values, and $12.2 \times$ less constraints when utilizing double precision floating-point values. We demonstrate the practicability of ZKLP by building a protocol for privacy preserving peer-to-peer proximity testing - Alice can test if she is close to Bob by receiving a single message, without either party revealing any other information about their location. In such a configuration, Bob can create a proof of (non-)proximity in $0.26 s$, whereas Alice can verify her distance to about $470$ peers per second
Cryptographic primitives have finite security lifespans, yet many modern systems—particularly blockchain and zero-knowledge infrastructures—are structurally resistant to change. Existing guidance on cryptographic agility assumes mutable environments and does not account for immutability, governance overhead, or proof system constraints. This work introduces a framework for evaluating cryptographic agility in deployed systems. We propose the Cryptographic Agility Score (CAS), a nine-dimensional evaluation model that characterizes how and where cryptographic dependencies are embedded, and how these constraints affect migration feasibility. Applying the framework to TLS and ZK rollup systems reveals a structural distinction: systems that anchor cryptographic primitives at negotiation layers achieve agility, while those that anchor them at execution layers face fundamental constraints. In ZK systems, these constraints are not only architectural but mathematical, arising from the algebraic structure of proof systems. This preprint presents the framework and its application across case studies. It is intended as a diagnostic and design tool for engineers building systems that must remain resilient under cryptographic change.
Tor's original design does not have an incentive mechanism but relies on volunteers to maintain their relay nodes for free, eventually leading to the current situation of centralization and lack of relay nodes. Current incentive schemes designed for Tor generally rely on centralized roles, thus presenting a risk of destroying Tor's anonymity. This paper proposes R2E, a decentralized scheme that treats Tor relay services as cryptocurrency mining and rewards the relays with generated tokens while addressing the challenge of how to design decentralized protocols that quantify workload while ensuring fairness and anonymity. We construct the Proof-of-Relay protocol in R2E that enforces random circuit selection, limits the number of nonce attempts, and exploits one-time keys and zero-knowledge proofs to protect participants' identities. We implemented a prototype of R2E based on Ethereum and conducted the trial operation and several confirmation experiments involving$2^{20}$clients,$2^{10}$to$2^{16}$nodes, and 256 circuits for each client to demonstrate its applicability. Analysis and experimental results show that R2E can effectively ensure the anonymity of participants' identities and fairness of incentive allocation while showing good performance in overhead and scalability, making it easy to be quickly applied in practical deployments.
Many Internet of Things (IoT) applications are considering multi-tenancy to support for multiple entities sharing access to the same IoT devices. The challenge of ensuring IoT security and privacy is exacerbated in multi-tenant environments accommodating “guest” users, i.e., opportunistic users that the system has not encountered. Thus, there is a need for novel access control mechanisms capable of addressing the complexities introduced by the opportunistic nature of the users who create complex trust relationships within the IoT ecosystem. In this study, we proposed a solution that leverages Verifiable Credentials (VCs) to implement Attribute-Based Access Control (ABA C) for multi-tenant IoT environments and we integrate it with W3C's Web of Things (WoT) standards, enhancing interoperability. Through the utilization of VCs, the solution provides secure verification and efficient revocation of user attributes, enabling access control decisions based on the enclosed attributes. Additionally, the proposed system ensures privacy, since users can selectively disclose the necessary attributes to gain access to resources through the utilization of Zero Knowledge Proofs (ZKPs). Finally, the solution does not require users to have any “pre-existing” trust relationships with the protected system.
Chaehyeon Lee, Jonathan Heiss, Stefan Tai, James Won‐Ki Hong
Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.
With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge. This study explores the intersection of reinforcement learning and data privacy, specifically addressing the Multi-Armed Bandit (MAB) problem with the Upper Confidence Bound (UCB) algorithm. We introduce zkUCB, an innovative algorithm that employs the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) to enhance UCB. zkUCB is carefully designed to safeguard the confidentiality of training data and algorithmic parameters, ensuring transparent UCB decision-making. Experiments highlight zkUCB's superior performance, attributing its enhanced reward to judicious quantization bit usage that reduces information entropy in the decision-making process. zkUCB's proof size and verification time scale linearly with the execution steps of zkUCB. This showcases zkUCB's adept balance between data security and operational efficiency. This approach contributes significantly to the ongoing discourse on reinforcing data privacy in complex decision-making processes, offering a promising solution for privacy-sensitive applications.
Bing-Jyue Chen, Suppakit Waiwitlikhit, Ion Stoica, Daniel Kang
Machine learning (ML) is increasingly used behind closed systems and APIs to make important decisions. For example, social media uses ML-based recommendation algorithms to decide what to show users, and millions of people pay to use ChatGPT for information every day. Because ML is deployed behind these closed systems, there are increasing calls for transparency, such as releasing model weights. However, these service providers have legitimate reasons not to release this information, including for privacy and trade secrets. To bridge this gap, recent work has proposed using zero-knowledge proofs (specifically a form called ZK-SNARKs) for certifying computation with private models but has only been applied to unrealistically small models.
The rapid development of large machine learning (ML) models requires a massive amount of training data, resulting in booming demands of data sharing and trading through data markets. Traditional centralized data markets suffer from low level of security, and emerging decentralized platforms are faced with efficiency and privacy challenges. In this paper, we propose OmniLytics+, the first decentralized data market, built upon blockchain and smart contract technologies, to simultaneously achieve 1) data (resp., model) privacy for the data (resp. model) owner; 2) robustness against malicious data owners; 3) efficient data validation and aggregation. Specifically, adopting the zero-knowledge (ZK) rollup paradigm, OmniLytics+ proposes to secret share encrypted local gradients, computed from the encrypted global model, with a set of untrusted off-chain servers, who collaboratively generate a ZK proof on the validity of the gradient. In this way, the storage and processing overheads are securely offloaded from blockchain verifiers, significantly improving the privacy, efficiency, and affordability over existing rollup solutions. We implement the proposed OmniLytics+ data market as an Ethereum smart contract [41]. Extensive experiments demonstrate the effectiveness of OmniLytics+ in training large ML models in presence of malicious data owner, and the substantial advantages of OmniLytics+ in gas cost and execution time over baselines.
Boyuan Feng, Zheng Wang, Yuke Wang, Shu Yang · 5 authors
Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the performance of zkSNARK NNs is far from optimal due to the million-scale circuit computation with heavy scalar-level dependency. In this paper, we propose a type-based optimizing framework for efficient zero-knowledge NN inference, namely ZENO (ZEro knowledge Neural network Optimizer). We first introduce ZENO language construct to maintain high-level semantics and the type information (e.g., privacy and tensor) for allowing more aggressive optimizations. We then propose privacy-type driven and tensor-type driven optimizations to further optimize the generated zkSNARK circuit. Finally, we design a set of NN-centric system optimizations to further accelerate zkSNARK NNs. Experimental results show that ZENO achieves up to 8.5× end-to-end speedup than state-of-the-art zkSNARK NNs. We reduce proof time for VGG16 from 6 minutes to 48 seconds, which makes zkSNARK NNs practical.
Purpose This study aims to examine the impact of consumer risk appetite, biases (specifically negative recency bias), and the importance of reviews in enhancing information quality. By analyzing these variables, the authors gain insights into their role in enriching the overall information spectrum available to consumers. The findings contribute to a better understanding of how risk appetite, biases and consumer reviews shape the quality of information. Design/methodology/approach The questionnaire assessed the relationship between dependent and independent variables by asking participants to rate their experiences in relevant scenarios. Variance-based structural equation modeling with the ADANCO program was used to examine the data. ADANCO software is used explicitly for variance-based structural equation modeling. To evaluate research models and test hypotheses, partial least square path modeling is used. Findings The efficiency of reviews and ratings is greatly influenced by consumer risk appetite. Businesses should focus on clients who are willing to take risks and balance positive and negative feedback. It is essential to comprehend how customers understand reviews. Credibility is increased by taking biases into account and encouraging unbiased criticism. Promoting thorough reviews strengthens influence. Monitoring and making use of these elements improve online reputation and commercial success. Research limitations/implications The research has limitations due to the simplicity of the attributes taken into account and the requirement for a larger sample size. Overcoming barriers to promote consistent client feedback is essential, and tailored emails can help with assessment generation. Increased customer participation in writing evaluations can be achieved by removing obstacles and highlighting the advantages of participation. Originality/value Businesses and buyers rely on this “organically” generated content as the basis of their promotional strategy and buying decisions. Most of the research is related to consumer reviews, their behavior and the importance of social validation. However, some critical aspects related to this need further investigation.
Unlabelled: The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems adopt data-driven paradigms for precision medicine and clinical decision support, the need for secure, privacy-preserving, and collaborative learning frameworks has become critical. This tutorial introduces a comprehensive, clinically oriented, and compliance-aware framework integrating federated learning (FL) and blockchain for secure and privacy-preserving health care analytics. FL enables collaborative training across distributed institutions without raw data sharing, in alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). However, FL remains vulnerable to model poisoning and gradient leakage. To address these risks, we introduce blockchain-based FL (BCFL), which leverages blockchain's immutable ledger and decentralized consensus to enhance trust, verifiability, and auditability. The tutorial's main contributions include (1) a taxonomy of diverse medical data types and their FL requirements; (2) three integration architectures (fully coupled, semicoupled, and loosely coupled) analyzed for security, scalability, and regulatory compliance; (3) a security analysis of health care-specific vulnerabilities and mitigation strategies using advanced cryptography, such as zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and United States Food and Drug Administration guidelines for AI-enabled medical devices. We demonstrate BCFL's relevance across major health care applications, including disease prediction, medical imaging, patient monitoring, and drug discovery, and highlight emerging research directions such as quantum-resilient cryptography, scalable interoperability, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, and collaborative AI in health care; fostering privacy-preserving analytics; and improving patient outcomes.
Cryptocurrency, a decentralized frame of computerized cash, has picked up ubiquity around the world. In any case, its broad selection has brought consideration to noteworthy security concerns. This paper presents a exhaustive examination of cryptocurrency security, distinguishing key challenges and proposing arrangements to support the security of advanced assets. The paper starts by talking about the foundational innovation of cryptocurrencies, specifically blockchain, which offers straightforwardness and unchanging nature but is defenceless to assaults such as 51% assaults and double-spending. It at that point digs into security dangers related with cryptocurrency capacity and trade stages, counting wallet vulnerabilities and hacking incidents. Current security best hones, such as multi-signature wallets and cold storage solutions, are analysed, alongside rising innovations like zero-knowledge proofs and homomorphic encryption. Furthermore, the part of administrative systems in advancing cryptocurrency security is investigated, highlighting the require for a adjusted approach that energizes advancement whereas securing investors. In conclusion, guaranteeing the security of cryptocurrencies is basic for cultivating believe and widespread adoption. By tending to vulnerabilities and executing vigorous security measures, partners can relieve dangers and open the complete potential of advanced monetary forms. This paper contributes profitable experiences to the continuous discourse on cryptocurrency security and recommends roads for future inquire about in this energetic field.
In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding & Identity Management, Data Integrity & Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.
Zero-knowledge proofs (ZKPs) are computationally demanding to generate. Their importance for applications like ZK-Rollups has prompted some to outsource ZKP generation to a market of specialized provers. However, existing market designs either do not fit the ZKP setting or lack formal description and analysis. In this work, we propose a formal ZKP market model that captures the interactions between users submitting ZKP tasks and provers competing to generate proofs. Building on this model, we introduce $Proo\varphi$, an auction-based ZKP market mechanism. We prove that $Proo\varphi$ is incentive compatible for users and provers, and budget balanced. We augment $Proo\varphi$ with system-level designs to address the practical challenges of our setting, such as Sybil attacks, misreporting of prover capacity, and collusion. We analyze our system-level designs and show how they can mitigate the various security concerns.
Zero-knowledge proofs (ZKPs) are computationally demanding to generate. Their importance for applications like ZK-Rollups has prompted some to outsource ZKP generation to a market of specialized provers. However, existing market designs either do not fit the ZKP setting or lack formal description and analysis. In this work, we propose a formal ZKP market model that captures the interactions between users submitting ZKP tasks and provers competing to generate proofs. Building on this model, we introduce $Prooφ$, an auction-based ZKP market mechanism. We prove that $Prooφ$ is incentive compatible for users and provers, and budget balanced. We augment $Prooφ$ with system-level designs to address the practical challenges of our setting, such as Sybil attacks, misreporting of prover capacity, and collusion. We analyze our system-level designs and show how they can mitigate the various security concerns.