Follow blockchain research across journals, conferences, and preprint repositories.
4,146 results · page 22 of 173
Subhasis Thakur, Martín Serrano
No abstract is available for this record.
Sachdeva Ks
Digital identity is critical, yet centralized providers create single points of failure—breaches have exposed billions of records—and quantum computing threatens the classical public-key cryptography (RSA/ECC) on which these systems rely. We present a system-level integration of blockchain, zero-knowledge proofs (ZKPs), and post-quantum cryptography (PQC) for privacy-preserving digital identity. A blockchain-based decentralized identifier (DID) system removes central databases; all signing and key-encapsulation operations use lattice-based PQC (CRYSTALS-Dilithium and Kyber); and selective disclosure is provided by Groth16 zk-SNARKs, with revocation via on-chain Merkle non-membership accumulators. We specify the full credential lifecycle—issuance, two-phase authentication, and revocation—with an explicit trust boundary separating the in-circuit Groth16 relation from the off-circuit issuer-signature check. We report a measured evaluation on a reference prototype: under liboqs 0.15.0, Dilithium-II signs/verifies in 0.19/0.06 ms and Kyber-512 encapsulates/decapsulates in 0.018/0.022 ms; a single-authentication Groth16 proof over the 21,715-constraint BN254 credential circuit takes <inline-formula> <tex-math notation="LaTeX">$\approx 981$ </tex-math></inline-formula> ms (snarkJS) and <inline-formula> <tex-math notation="LaTeX">$\approx 177$ </tex-math></inline-formula> ms (native rapidsnark) on byte-identical inputs, with <inline-formula> <tex-math notation="LaTeX">$\approx 40$ </tex-math></inline-formula> ms verification, a 723-byte proof, and <inline-formula> <tex-math notation="LaTeX">$\approx 243$ </tex-math></inline-formula>,000 gas for on-chain verification on a local EVM. A lifecycle harness with a passing revoked-credential negative test validates correctness. The signing and key-encapsulation layers are quantum-safe under current lattice assumptions; the Groth16 proof layer is classically secure only, and its post-quantum migration is identified as future work. End-to-end credential unforgeability is conditioned on an honest holder wallet performing the off-circuit signature check (Assumption 5). Every quantitative claim is labelled measured [M], simulated [S], assumption [A], or future work [F].
Octavian Untila
Current approaches to verifying AI training data compliance face a fundamental tension: copyright holders need to know whether their content was used in training (EU AI Act, Article 53(1)(d)), while model providers need to protect their training data as trade secrets (GDPR, trade secret law). Existing zero-knowledge proof systems for machine learning (ZKML) address this partially by providing proofs of non-membership for exact data points. However, real-world training pipelines involve tokenization, chunking, paraphrasing, and augmentation, rendering exact-match proofs insufficient. We identify a gap in the literature: no existing system combines semantic fingerprinting with zero-knowledge proofs to enable semantic non-membership verification. We propose an architecture for Zero-Knowledge Semantic Non-Membership (ZK-SNM) that enables a model provider to prove, without revealing any training data, that no document in their training corpus is semantically similar to a queried document above a specified threshold. We discuss the technical challenges, including the computational cost of similarity search within ZK circuits, and propose mitigation strategies based on locality-sensitive hashing and hierarchical verification. This position paper establishes the problem formulation and proposed architecture; experimental validation is left to subsequent work.
Christopher Schulze
GLYPH is a transparent verification layer for Ethereum for trustless on-chain verification of heterogeneous proof systems. It unifies upstream SNARK and STARK settlement through a single packed arity-8 sumcheck verifier over p = 2^128 - 159, while preserving upstream assumptions. The design centers on a universal adapter surface, UCIR compilation, and a chain-bound artifact interface for stateless verification. Benchmark evidence in the whitepaper reports 29.45k total transaction gas in recorded testnet receipts. This record includes the whitepaper and the formal proof appendix.
Pankaj Kumar, Arun K H, Yogesh N, Prakash Babu · 8 authors
The growing dependance on data-based decisionmaking in healthcare has brought attention to the vital importance of secure, privacy-preserving and collaborative learning techniques. Traditional centralized learning approaches in medical data often raise concerns regarding patient privacy data leaks and even regulatory troubles. Federated learning came as a good solution, where it allows model training in different hospitals without sharing the sensitive patient data. However, federated learning has its problems - it can be mislead with fake updates, the model can even be poisoned and it is really hard to trust every participants involved. In this work, we present fed-chain, a secure and scalable framework which brings together federated learning, blockchain and zero-knowledge-proofs(ZKPs) preserving the privacy of patient's data in healthcare. Blockchain here adds decentralized trust, immutability and makes model updates transparent to review while ZKPs helps in proving correctness without leaking personal data. We are implemented this framework for heart disease prediction where multiple hospitals train the model together but the data stays confidential. Our experimental results shown better accuracy, more strength against attacks and even low communication cost compared to other FL setups. Overall, the systems gives a safer approach for working together on healthcare data, allowing hospitals and research centers to generate valuable predictions using these models while keeping the patient data private and safe.
Mohammad Fairus Bin Zulkifli, Rabiah Abdul Kadir, mohamad nazir ahmad
Growing reliance on digital knowledge sharing across academic, corporate, and public sectors has raised serious concerns about data integrity, trust, and security. Blockchain consensus mecha-nisms offer a promising path forward through decentralized, transparent, and tamper-proof frameworks. This systematic review examines how these mechanisms enhance trust in knowledge sharing platforms, focusing on four directions: how these mechanisms are applied within knowledge sharing con-texts, the challenges they introduce for knowledge sharing de-ployment, and the advantages they provide to trust-based knowledge sharing ecosystems. Following PRISMA 2020 guide-lines, three databases Scopus, IEEE Xplore, and Web of Science were searched, and peer-reviewed studies published between 2020 and 2025 were selected for analysis. In terms of knowledge sharing applications, blockchain consensus mechanisms build trust through multiple co-occurring pathways, including distrib-uted verification, transparency, cryptographic security, immu-tability, incentive alignment, and smart contract automation. Algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, and Byzantine Fault Tolerance variants are widely adopted, each offering different trade-offs between secu-rity, efficiency, and scalability. In terms of challenges, scalabil-ity, energy consumption, and integration complexity with exist-ing systems remain the most significant barriers to adoption. In terms of advantages, blockchain consistently delivers stronger data security, greater transparency, and reduced dependence on centralized authorities across knowledge sharing contexts. This review concludes that blockchain consensus mechanisms offer layered and compounding trust benefits, yet technical and or-ganizational barriers continue to limit widespread deployment. Future research should focus on energy-efficient protocols, scalable architectures, and real-world effectiveness studies.
Dr.K.Rekhadevi Dr.K.Rekhadevi, METHUKUPALLY SUSHMITHA, GUNDU ARYAN, GURRAM SREEJA · 5 authors
The fast growth of the Internet of Medical Things (IoMT) has made it much harder to manage and protect medical data because of security and privacy issues. This paper suggests a secure federated cloud storage system that uses a hybrid heuristic attribute-based encryption (ABE) scheme combined with a permissioned Blockchain to solve these problems. The suggested system improves data privacy and integrity by first gathering medical data and then encrypting it with ABE using the best key made by the Hybrid Mexican Axolotl with Energy Valley Optimizer (HMO-EVO). A permissioned blockchain securely stores the encrypted data, making sure that access is tightly controlled and that data breaches are avoided. The system uses federated learning with a Multi-scale Bi-Long Short-Term Memory and Gated Recurrent Unit (MBiLSTM-GRU) to make accurate predictions about diseases. This helps with healthcare monitoring. This federated approach lets deep learning models be trained in different places, keeping patient data private while still allowing for collective learning. The experimental results demonstrate that the proposed system surpasses traditional methods regarding security, efficiency, and predictive accuracy. This study presents an extensive framework for the secure management of medical data, integrating the advantages of federated learning and blockchain technology to tackle the essential challenges of data ownership, regulatory adherence, and privacy within IoMT networks.
Yixiao Zheng, Changzheng Wei, Xiaodong Qi, Hanghang Wu · 12 authors
In Vertical Federated Learning (VFL), prior work has primarily focused on protecting data privacy, while overlooking the risk that participants may manipulate local model execution to mount integrity attacks.Integrating zero-knowledge proofs (ZKPs) into the training process can ensure that each party's computations are verifiable without revealing private data.However, directly encoding deep model training as a monolithic ZKP circuit is impractical due to: (i) complex circuit design and high overhead from frequent parameter commitments, (ii) expensive proof generation for embeddings(cross-party information interface), and (iii) synchronous proof generation that blocks iterative training rounds.To address these challenges, we present ZKSL, an efficient and asynchronous VFL framework that achieves verifiable training under a malicious threat model.ZKSL partitions deep neural networks into layer-wise circuits and generates their proofs in parallel, ensuring input-output consistency via Privacy-Commitment PLONK (PC-PLONK), a lightweight extension that supports low-cost, iteration-by-iteration parameter commitments.For embedding layers, ZKSL adopts a probabilistic verification technique that reduces proof complexity from O(N nd) to O(nd).Furthermore, ZKSL incorporates an asynchronous compute-prove scheduling mechanism to decouple proof generation from training iterations, effectively mitigating pipeline stalls.Experimental results on DeepFM and CNN models show that ZKSL reduces proof generation time by up to 73% while maintaining 99.4% accuracy, demonstrating superior scalability and practicality for real-world federated learning.
Fan Gao
In the digital economy era, the contradiction between data sharing and privacy protection is increasingly prominent. Traditional privacy protection technologies such as anonymization, differential privacy, and homomorphic encryption are difficult to meet the diverse privacy demands in multiple fields due to their flaws such as vulnerability to cracking, sacrificing data accuracy, and high computational complexity. Zero-knowledge proof (ZKP), with its core advantage of "data available but not visible", has become the key technical support to solve this contradiction. However, the current protocol types are complex and the demands in various fields vary significantly, leading to confusion in protocol selection and imbalance in resource allocation in practice. This paper systematically reviews the theoretical basis, technical system and mainstream protocol characteristics of zero-knowledge proof, integrates the advantages of demand-driven decision tree and protocol three-dimensional classification model, constructs a protocol selection framework of "demand-driven - feature matching - dynamic optimization", and clarifies the multi-stage decision-making process; Four typical fields, namely education, machine learning, finance, and healthcare, were selected to verify the effectiveness and universality of the framework, providing standardized tools for the large-scale application of zero-knowledge proof technology in various fields and direction guidance for the development of privacy protection technology in the post-quantum era.
Alen Horvat, Steffen Schwalm, Johannes Sedlmeir, Hakan Yildiz
Among the privacy-enhancing technologies explored in the context of the European Digital Identity (EUDI) Wallets, zero-knowledge proofs stand out for their ability to maintain established levels of cryptographic verifiability while enabling data minimisation – relative to the requirements expressed by the relying party (RP). However, legal frameworks in many sectors require the collection of verifiable data beyond the RP’s immediate needs, which may substantially narrow down the scope of data minimisation that can be achieved in regulated domains. Accordingly, this paper examines the tensions between the strict data minimisation requirements for the EUDI Wallets and the extensive legal proof obligations that relying parties must fulfil. Our analysis of the regulatory foundations and relevant technical mechanisms identifies documentation, audit, and long-term preservation obligations as key sources for friction. We explore the implications of the corresponding tensions, point to gaps in current standardisation and compliance regimes, and suggest potential technical and non-technical solution approaches that could help reap the benefits advanced privacy-enhancing technologies can offer in practice.
Jeffery Podolski
This study presents a novel framework for integrating algorithmic audit trails into cloud banking compliance systems through the application of zero-knowledge proofs (ZKPs), addressing the critical tension between regulatory transparency and data privacy. The increasing migration of banking operations to cloud infrastructures has intensified the need for robust audit mechanisms that can verify compliance with financial regulations—such as anti-money laundering (AML) and know-your-customer (KYC) mandates—without exposing sensitive client information or proprietary risk models. Traditional audit trails, which rely on logging and storing plaintext transactional data, introduce significant vulnerabilities, including data breaches, unauthorized access, and inconsistencies across distributed cloud environments. Conversely, pure cryptographic anonymization can render audits meaningless by obscuring the provenance and integrity of records. The proposed architecture leverages ZKPs to allow an auditor (a regulatory body or internal compliance officer) to verify that a set of transactions adheres to predefined compliance rules without ever accessing the underlying data. In this system, each financial transaction executed within a cloud banking platform generates a cryptographic commitment. This commitment, along with a zero-knowledge proof, attests that the transaction satisfies all applicable regulatory constraints—such as limits on transaction value, jurisdictional restrictions, or multi-factor authentication requirements—without revealing the specific account numbers, personal identifiers, or the exact transaction details. The proof is constructed using succinct non-interactive zero-knowledge arguments (zk SNARKs), which provide both scalability and computational efficiency suitable for high-frequency transaction environments. The framework is built upon a dual-layer architecture. The first layer, the compliance engine, resides within the cloud banking application and is responsible for executing transactions, generating the corresponding ZK proofs, and committing the hashed transaction data to an immutable, decentralized ledger—a permissioned blockchain. This ledger serves as the algorithmic audit trail, recording only the cryptographic commitments and the proofs. The second layer, the audit interface, is accessible to authorized auditors. When an audit is required, the auditor submits a verification request against a specific range of transactions. The system retrieves the relevant commitments and proofs from the blockchain and runs a verification algorithm. The output is a simple Boolean result: either all transactions in the requested range are compliant, or the proof fails, triggering a detailed exception process. Importantly, the auditor gains no knowledge of the underlying transaction data; they only learn whether the regulatory predicates have been met. To realize this framework, the study identifies and addresses three principal challenges: proof generation overhead, scalability within cloud environments, and the integration of dynamic regulatory rules. Proof generation, particularly for complex compliance rules, can be computationally intensive. The proposed solution employs a hybrid approach: pre-computed proof templates for standard compliance checks (e.g., transaction size limits) are cached and reused, while complex, multi-condition checks (e.g., suspicious activity reporting rules) generate proofs on-the-fly using optimized multi-party computation techniques. For scalability, the audit trail ledger utilizes a sharded blockchain architecture, where transaction records are partitioned across multiple parallel chains based on geographic region or transaction type, ensuring that proof verification can be performed concurrently without a single bottleneck. Dynamic regulatory rules are accommodated through a modular smart contract layer. When a regulation changes, the underlying compliance predicate is updated on the blockchain, and all subsequent proofs are generated against the new rule. Existing valid proofs remain immutable, providing a historical record of compliance at the time of each transaction
Subhasis Thakur, John Breslin
No abstract is available for this record.
Amirhossein Taherpour, Xiaodong Wang
Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts (EDSCs) and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.
Hao Wu, HU Rui, Yebo Feng, LIU Yu · 6 authors
Large-scale privacy-preserving computation is essential for cross-domain data collaboration, making correctness auditing and accountability crucial for practical deployment. Existing auditing schemes typically use zero-knowledge proofs (ZKPs) to verify computation correctness and blockchain-based stake or reputation mechanisms to constrain dishonest participants. However, ZKP approaches often verify computation tasks or circuit constraints independently, causing high communication and verification overhead in large-scale task scenarios. Meanwhile, existing blockchain-based accountability mechanisms are weakly coupled with cryptographic audit results and lack sufficient adaptability in dynamic environments. In this paper, we propose MTBA, a batch auditing framework for privacy-preserving computation that leverages VOLE correlation to support polynomial proof generation and integrates blockchain-based reputation feedback. MTBA transforms arithmetic-circuit computations into auditable polynomial tasks by encoding multiplication-gate consistency as polynomial relations, converts these tasks into compact polynomial proofs, and introduces polynomial proof aggregation at multi-task for batch auditing. It further records signed audit outcomes on blockchain to support adaptive reputation feedback and malicious party accountability. Experimental results on circuits with up to $10^{7}$ multiplication gates show that MTBA improves auditing throughput by up to 2.7 times faster than conventional independent auditing schemes. These results indicate that MTBA provides a scalable and accountable auditing mechanism for large-scale privacy-preserving computation.
Alan Xiao
Proof-of-useful-work (PoUW) replaces the wasted computation of proof-of-work with valuable tasks such as machine-learning inference, but has historically failed on the verification asymmetry: useful work is as costly to verify as to perform. Zero-knowledge machine learning (zkML) removes the asymmetry cryptographically, yet proving overheads currently preclude verifying every job. We analyze a sampled-audit design in which each committed job is audited independently with probability p after commitment, escalating from cheap re-execution to a zkML dispute court, with detected fraud slashing a stake S. We prove that economic security reduces to one scale-invariant bound, S > c/p, where c is the cost of one job: the attacker's expected profit from fabricating k results is then strictly decreasing in k, so the optimal attack is a single fabricated job, and it is unprofitable. The bound is invariant to batch size and robust to exit scams, Sybil splitting, and audit-gap hunting; Monte Carlo simulation validates all claims. We further prove exponential fragility under randomness grinding, detection collapses to p g with g candidate seeds, making unbiasable randomness a hard requirement, and derive the cost-optimal sampling rate p * = δ /κ.
Pierpaolo Della Monica, Ivan Visconti, Andrea Vitaletti, Marco Zecchini
An essential requirement for the large-scale adoption of Web3 is enabling users to benefit from their data even within already deployed systems. This raises an important open question: how can existing, widely adopted software verify that a user has retrieved specific data from a TLS server? Impressive scientific results (e.g., DECO [CCS20] and the work of Xie et al. [USENIX24]) and industrial products (TLSNotary) have recently made progress in the above challenging direction. However, while they nicely leave TLS servers untouched, the retrieved data is then used in computations with verifiers that are required to run some advanced non-standardized cryptographic schemes (e.g., ZK-SNARKs), which clearly limits the large-scale adoption of the proposed technologies. In this paper, building on top of previous approaches and relying on the recent concept of Predicate Blind Signatures of Fuchsbauer and Wolf [Eurocrypt24], we bypass the limits of prior work by presenting ACTS a distributed architecture that, while still leaving TLS servers untouched, it allows a user to show possession of data retrieved from TLS servers simply requiring that the software of the verifier can check a standard signature. Our contributions include a round-optimal predicate blind signature protocol that produces standard RSA-PSS signatures. We show how this primitive can be integrated into the DECO architecture (and its successors) to certify data retrieved from TLS servers. Furthermore, we have optimized our construction to make it practical on commodity hardware for a large and significant class of policies implemented by the notary (i.e., the actor that is in charge of obliviously certifying TLS data, therefore preserving data confidentiality). We provide an experimental evaluation on the simple but powerful enough use case of a PDF document downloaded from a TLS server and encoded into an AES-GCM ciphertext. The user will then get a certified PDF through a standard PADES signature added obliviously to the PDF along with some metadata by a notary service. The resulting standard signed PDF document can be transparently verified using off-the-shelf PDF readers. Our experimental validation demonstrates that our architecture is suitable for real-world deployment in concrete scenarios.
Jakub Cwirlej
No abstract is available for this record.
Balram P, Lakshmi Rai, Mansi Kodag
ZKVault is a privacy-preserving decentralized KYC system aimed at eliminating redundant identity verification and reducing the risks associated with centralized data storage. The project leverages zero-knowledge proofs and blockchain technology to enable users to prove specific identity attributes without exposing sensitive personal information. It ensures secure, tamper-proof, and reusable verification across multiple platforms while maintaining user privacy and regulatory compliance. Experimental results demonstrate efficient performance, with proof generation taking approximately 1.8 seconds and verification under 50 milliseconds, making ZKVault a scalable and practical alternative to traditional KYC systems.
Ping Ji, Haijie Wang
In the face of the regulatory failure problem caused by blockchain hidden addresses, existing solutions often fall into a dilemma where 'privacy protection' and 'compliance review' are either one or the other.This paper proposes an innovative integration framework that transforms the behavioural elements in anti-money laundering and other legal provisions (such as 'high-frequency and small-scale transactions') into computable logic.Based on zero-knowledge proof technology, it generates verifiable credentials to determine whether the transaction behaviour is compliant without revealing the true identity of the address.Experiments on a public blockchain transaction dataset (elliptic) show that this framework achieves an average improvement of over 15% in core identification performance compared to traditional non-private rule-based methods, while maintaining an acceptable performance overhead.As a proof-of-concept validation conducted on a transparent dataset with simulated concealment, the actual performance may differ in native privacy-preserving chains.This research provides a new approach that combines legal rigor with technical feasibility for achieving effective on-chain behaviour supervision while protecting user privacy.
Ferit Gezgil
We propose Proof of Witness (PoWit), a novel consensus mechanism for digital currency that replaces energy-intensive mining and capital-based staking with independent third-party witness verification. In PoWit, each transaction requires cryptographic signatures from three parties: sender, receiver, and a randomly selected witness. The witness validates the sender’s balance and transaction history before signing, eliminating the need for global consensus while maintaining security guarantees. Our simulation with 10,000 users demonstrates 100% double-spending prevention (n = 10, 000, 99% CI [99.93%, 100%]), 113.9 transactions per second, and complete chain integrity. The non-selective witness assignment achieves theoretical randomness with only 0.27% deviation, making collusion attacks impractical. PoWit offers a sustainable alternative to Proof of Work and Proof of Stake, with significantly lower energy consumption and fairer participation model.
Marc Herdina
The evolution of the World Wide Web is conventionally described in three eras-Web1 (read), Web2 (read-write), and Web3 (read-write-own)-each of which relocated a distinct form of power along the value chain: from publishers, to platforms, to ledger-anchored token holders. This paper introduces and defends a fourth: Web4 (read-write-own-act), in which sovereign ownership is not confined to tokens or ledger records but is co-specified across every operational plane of the network-identity, physical connectivity, compute, naming, developer tooling, application delivery, and consumer hardware-and in which autonomous agents act on behalf of that ownership under cryptographic, time-boxed capability grants. The central original contribution is the Ownership Thesis: the axiom that every layer of the stack must be reducible to a claim controlled by a decentralized identifier the subject holds directly, not one that is issued or held by an operator on the subject's behalf. From this axiom we derive a seven-layer reference architecture and formalize the Synthetic Web as the class of network activity structurally defined by agent-executed action under such capability grants. We position each layer against the relevant standards (W3C DID Core v1.1, W3C Verifiable Credentials Data Model v2.0, Model Context Protocol, Agent-to-Agent v1.0.0, NIST CAISI AI Agent Standards Initiative) and provide a security analysis covering key management, alias unlinkability via BBS+ selective disclosure, Sybil resistance, delegated-authority scope creep, and data-sovereignty enforcement, with explicit identification of open problems that require further verification.
nithin nagineni
Zero-Knowledge Proofs (ZKPs) have emerged as a transformative cryptographic primitive enabling one party to prove the validity of a statement without revealing any underlying information. This property has made ZKPs a cornerstone in privacypreserving systems, particularly in blockchain, authentication protocols, and secure distributed computing. Despite rapid advancements in ZKP frameworks such as zk-SNARKs and zk-STARKs, the testing and validation of these systems remain a critical challenge. The complexity of arithmetic circuits, the probabilistic nature of proofs, and the potential for subtle vulnerabilities necessitate robust testing methodologies. This paper presents a comprehensive review of testing techniques for Zero-Knowledge Proof systems. It begins by outlining the fundamental properties of ZKPs, including completeness, soundness, and zero-knowledge, which form the basis for testing correctness and security. The study then explores existing literature on testing approaches, including formal verification, fuzz testing, constraint validation, and symbolic execution. Recent research highlights that vulnerabilities such as underconstrained circuits account for a significant portion of ZKP system failures, emphasizing the need for systematic testing strategies. Furthermore, this review categorizes testing techniques into functional, security, and performance testing, providing a structured understanding of their roles in ZKP validation. A comparative analysis of existing methods is also presented to evaluate their effectiveness, scalability, and limitations. The discussion identifies gaps in current research, particularly in automated testing frameworks and standardized benchmarking. The paper concludes by emphasizing the importance of integrating advanced testing methodologies into ZKP development pipelines to ensure reliability, scalability, and security. Future research directions include AI-driven testing, hybrid verification models, and improved tooling for circuit validation. This review aims to serve as a foundational reference for researchers and practitioners working on secure and efficient ZKP systems.
Matěj Hůlek
Zero-knowledge důkazy (ZKP) umožňují dokazovateli přesvědčit ověřovatele o správnosti tvrzení, aniž by odhalil podkladový svědek. V posledních letech se ZKP stal klíčovou součástí aplikací chránících soukromí i škálovatelných blockchainových systémů, od stručných rollupů založených na SNARK až po transparentní konstrukce STARK založené na hašovacích funkcích a důkazy vykonání v systémech zkVM/zkEVM. Tato diplomová práce zkoumá hlavní moderní rodiny ZKP, včetně pairing-based zk-SNARKů, Plonkish protokolů, zk-STARKů, Bulletproofs a novějších hash-based přístupů k proximity testingu, jako je WHIR, se zaměřením na jejich kryptografické stavební bloky, bezpečnostní předpoklady a praktické kompromisy. Práce dále mapuje existující benchmarkingové přístupy a identifikuje běžné problémy, které komplikují spravedlivé porovnání, včetně nekonzistentních benchmarkových úloh, heterogenní volby parametrů a nereprodukovatelných běhových \\ prostředí. Na základě této analýzy práce vymezuje klíčové hodnoticí metriky a navrhuje sjednocený benchmarkingový framework typu host-agent s kontejnerizovanými provery, explicitními run manifesty, standardizovanými kontrakty výsledků a podporou jak pro CPU, tak pro GPU výpočetní cesty napříč systémy založenými na obvodech i zkVM. S využitím tohoto frameworku práce hodnotí reprezentativní implementace na sadě sémanticky porovnatelných primárních benchmarkových úloh a sekundárních benchmarkových úloh orientovaných na specializaci. Výsledky ukazují, že neexistuje jeden univerzálně nejlepší proof system: transparentní frameworky založené na obvodech, jako jsou Plonky3 a Winterfell, dosahují u primárních benchmarkových úloh nejlepších výsledků z hlediska doby generování důkazu a paměťových nároků, pairing-based systémy jako Groth16 a ICICLE poskytují nejmenší důkazy a nejrychlejší verifikaci pro scénáře omezené možnostmi ověřovatele nebo pro on-chain nasazení a systémy zkVM, jako jsou RISC Zero a SP1, směňují vyšší spotřebu paměti a větší důkazy za lepší programovatelnost a snazší integraci obecně použitelných výpočtů. Experimenty dále ukazují, že akcelerace pomocí GPU je nejpřínosnější u dostatečně velkých benchmarkových úloh, zatímco inicializační režie a limity VRAM její přínos u menších případů snižují. Celkově práce přispívá jak praktickým jednotným benchmarkingovým frameworkem, tak experimentálně podloženými doporučeními pro volbu ZKP přístupů v různých podmínkách nasazení, hardwaru, důvěryhodnostních modelů a bezpečnostních omezení.
Michal Vaňo
Federatívne učenie (FL) umožňuje spoločné trénovanie modelu bez priameho zdieľania údajov, ale často sa spolieha na silné predpoklady o čestnom správaní klienta a servera. To je dôvod, prečo štandardné FL protokoly poskytujú iba obmedzenú záruku ohľadom výpočtov na strane klienta, integrity odoslaných informácii, alebo ohľadom správnosti agregácie na strane servera. Táto diplomová práca skúma použitie systémov s nulovými znalosťami (ZKP) spolu s podpornými metódami na vytvorenie dôvery v FL prostredí. V tejto práci sa po úvode k FL a ZKP ďalej skúma prehľad existujúcich ZKP nástrojov v prostredí FL. Na základe tejto analýzy je vytvorená kategorizácia existujúcich prístupov FL založených na ZKP, ktorá je postavená najmä na cieľoch daného systému. Na základe identifikovaných možností zlepšenia práca navrhuje overiteľný protokol váženej agregácie. V tomto protokole je každý prijatý príspevok previazaný s autorizovanou váhou, prípustnou skrytou aktualizáciou, konzistentným váženým vstupom a výslednou aktualizáciou modelu, ktorú je možné verejne overiť prepočítaním. Tento protokol bol implementovaný ako prototyp s plne funkčnými kryptografickými komponentami. Následne je tento protokol vyhodnotený.