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.
Ahmet Ramazan Ağırtaş, Arda Buğra Özer, Zülfükar SAYGI, Oğuz Yayla
Unbiased and unpredictable randomness is a cornerstone of Web3 security, underpinning everything from consensus protocols to DeFi logic. Although Distributed Verifiable Random Functions (DVRFs) eliminate central points of failure, current designs often have to compromise performance. Most existing protocols are hindered by one of three limitations: proofs that scale linearly with the number of participants, high computational cost of bilinear pairings, or latency introduced by mandatory interactive steps during generation. In this work, we present Icy-DVRF, a protocol that improves DVRFwCP by employing a preprocessing scheme similar to FROST to reduce the number of interaction rounds among participants and lowering the additional communication cost from <inline-formula> <tex-math notation="LaTeX">$O(n^{2} t)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(t)$ </tex-math></inline-formula> while maintaining constant-size proofs. The downside of our construction is that, relative to DDH-DVRF and GLOW-DVRF, this approach incurs an additional off-chain communication round due to the threshold structure of our non-interactive zero-knowledge proof. This architecture ensures that verification costs remain low, regardless of the set of participants. While theoretical estimates suggest verification costs of approximately one quarter of those of standard designs, our empirical benchmarks on the Sepolia testnet, utilizing the EIP-2537: Precompile for BLS12-381 curve operations, confirm that Icy-DVRF requires only 88,803 gas for full execution. This represents a significant 43.02% reduction in total gas consumption compared to existing pairing-based constructions, saving 67,035 gas per on-chain verification. Off-chain, eliminating DVRFwCP’s Augmented Secure-DKG round yields a per-node speedup ranging from a factor of 1.46 at <inline-formula> <tex-math notation="LaTeX">$(n,t)=(5,3)$ </tex-math></inline-formula> to a factor of 4.43 at <inline-formula> <tex-math notation="LaTeX">$(n,t)=(50,34)$ </tex-math></inline-formula>.
Open access
Generative Adversarial Networks and Image Synthesis
Dr.B.Swathi Dr.B.Swathi, MOHAMMAD SANA, DAMERUPPULA SAI KIRAN, JADI GANESH · 5 authors
The quick rise of digital technologies has shown how blockchain could improve business operations by making them safer, more open, and less centralized. Most blockchain solutions, on the other hand, are made for big businesses, which makes it hard for small and medium-sized businesses (SMEs) to use them because they are too expensive, too complicated, and not modular. This study suggests a blockchain-based framework designed specifically for small and medium-sized businesses (SMEs) to make digital transformation more affordable. The framework includes stable consensus protocols, governance mechanisms, and important services like Decentralized Identity (DID), Zero-Knowledge Proofs (ZKP), and Digital Asset Management (DAM). It is meant to be modular, scalable, and simple to connect to current business systems. Experimental testing shows that SMEs are more efficient, secure, and easy to use. The proposed framework lowers the barriers to entry and lets small and medium-sized businesses use blockchain for new ideas, better operations, and safe online transactions.
In the coming 6G era, Internet of Consumer Electronics (ICE) is promising in academia and daily life. To improve the intelligence and reliability of ICE devices with limited resources, a cloud-edge-end collaborative intelligent architecture is designed. However, the current collaborative intelligent ICE system faces multiple security threats, e.g., illegal access to the intelligent data/model resources and poisoning/backdoor attacks during collaborative intelligent model training. The security of the intelligent collaborative ICE is still an open issue. To solve this problem, we propose a zero-knowledge proof (ZKP)-driven zero trust method to protect the security of the intelligent collaborative ICE. First, a zero-trust intelligent collaborative ICE security framework is established for the confidentiality and availability protection of data/model resources. Second, we propose a zero-trust multi-factor access control mechanism for mutual authentication and trust evaluation-based resource access authorization. Third, the ZKP-driven collaborative intelligent ICE mechanism is designed. In this mechanism, the selective differential privacy-based data preservation scheme and the zero-knowledge collaborative intelligent model training scheme are devised. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed secure intelligent collaborative ICE.
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.
Dharmesh Dhabliya, Aditya Lavhale, Sunil Thakur, R. M. Gomathi · 6 authors
In modern cryptography, improving the cryptographic security of Zero-Knowledge Proofs (ZKP) has become a compelling trend. Traditional models like the zk-SNARK and zk-STARK has shown strong security but are accompanied by the inherent issues of computational complexity and proof size. This work presents the Algebraic Zero-Knowledge Proof (AZKP) framework, using algebraic structures and integration of elliptic curves to optimize proof creation and verification. The suggested approach fills in key gaps found in the current methodologies, such as huge computational overhead and enormous proof sizes. Prime factorization in algebraic groups and ring homomorphisms of the AZKP framework is used to achieve small proof size without sacrificing computational efficiency. Comparing AZKP with zk-SNARK and zk-STARK models, experimental evaluation was applied to four critical performance metrics. generation time of proof, verification time, size of proof, and computational overhead. Results show that the AZKP is able to make a 48% decrease in proof generation duration and 20% increase in verification speed in comparison to zk-SNARK. Also, AZKP incurred lower computational cost than zk-STARK, with a proof size that is manageable. These results highlight the prospect of AZKP in cryptographic use where high-speed low-latency verification operations are desired. Further research will integrate AZKP in blockchain environments in order to increase real-time transaction validation.
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.
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.
This study analyzes 128,286 academic papers tagged as blockchain or cryptocurrency research by OpenAlex's machine-learning concept classifier, published between 2013 and mid-2026. A broader keyword search across paper abstracts identifies 1,938,409 publications that mention Web3-related terms. The analysis measures keyword frequency, temporal trajectories, growth rates, citation distributions, geographic concentration, institutional output, and open access rates. Key findings include 117x growth in annual blockchain publications between 2013 and 2025, the rise of zero-knowledge proofs as the fastest-growing cryptographic primitive (2.1x growth, 2025-2026 vs. 2022-2023), DeFi research experiencing a 74x increase from 2019 to 2025, NFT research peaking in 2023 before declining, China and India leading global output with 13.5% and 13.3% of all papers respectively, and 43.5% of all papers receiving zero citations.
Public distributed ledgers enforce integrity through radical transparency, creating tension with data minimization principles required for regulatory compliance. While Zero-Knowledge Proofs (ZKPs) offer a theoretical privacy solution, existing constructions often overlook adversarial constraints in smart contract environments. Specifically, the asynchronous decoupling of off-chain proof generation from on-chain submission introduces front-running and proof-reuse risks in public mempools. In this work, we formalize Selective Disclosure Authorization Schemes (SDAS), a cryptographic primitive for granular and revocable compliance checks on public ledgers without revealing the underlying witness. We define a security model for SDAS, introducing Ledger-Bound Attribute Unlinkability and Context-Aware Sender Binding to capture how valid proofs remain bound to their intended authorization context. To validate sender binding, we present ZK-Compliance, an Ethereum-based instantiation that operationalizes a user-controlled "Grant, Verify, Revoke" lifecycle. We implement the sender-binding component using a 14-constraint Circom circuit that anchors the zero-knowledge proof to the executing on-chain sender address. Our Sepolia evaluation confirms practical viability: browser-based proof generation executes in under 200 ms, and on-chain verification costs 240,512 gas, neutralizing proof reuse by different callers while preserving strict attribute privacy.
Nuha Omran Abokhdair, Ali Alissawi Ahmed AlQudairi
Decentralized storage networks increasingly rely on blockchain-based verification to ensure data integrity without centralized control; however, proof-intensive workloads introduce significant latency and on-chain cost overhead. This paper presents a lifecycle-based comparative analysis of major zero-knowledge proof (ZKP) models used in decentralized storage, focusing on zk-SNARK frameworks and transparent zk-STARK constructions. A multi-layer evaluation framework is introduced, aligning performance analysis with the core stages of the proof lifecycle: generation, aggregation, and on-chain verification. Building on this analysis, the paper proposes a hybrid architecture that combines parallel STARK-based proof generation with recursive SNARK-based compression, reducing on-chain verification complexity to near-constant. A Filecoin-inspired case study, supported by a quasi-empirical performance model, demonstrates that the proposed hybrid approach significantly reduces verification latency and data overhead while mitigating the linear growth of verification costs. The results indicate that hybrid ZKP architectures offer a scalable and economically viable solution for decentralized storage systems and large-scale blockchain networks. Keywords: Zero-knowledge proofs, zk-SNARKs, zk-STARKs, recursive aggregation, decentralized storage, verifiable cryptography, scalability, gas cost.
Low-altitude aerial networks play an increasingly critical role in urban logistics, emergency response, and intelligent airspace management. These networks must ensure reliable identity authentication while addressing privacy, efficiency, and security challenges. Traditional authentication mechanisms—such as PKI-based certificates and remote identification—either overexpose identity data or lack support for flexible attribute disclosure. Moreover, existing anonymous credential (AC) schemes, though offering privacy, often incur high computational and communication costs and exist vulnerable to replay attacks. To address these limitations, we proposeDVA-ACLF(Designated Verifier Asymmetric Anonymous Credential Scheme for Low-Altitude Flight), a lightweight credential system tailored for UAVs. DVA-ACLF leverages an efficient MAC-based construction combined with non-interactive zero-knowledge proofs to enable selective attribute disclosure without relying on pairings. It supports designated verification to prevent credential misuse and replay. Experimental results show that DVA-ACLF reduces credential size by 49% (2720 bits vs. 5369 bits in Idemix) and mitigates the linear growth of credential size with respect to attribute count observed in CL and BBS+ schemes. It requires only four single-base and$2(k-l)$double-base exponentiations on UAVs, where$k$is the number of selected attributes and$l$is the number of disclosed attributes—both typically small. These features make DVA-ACLF a strong candidate for secure, efficient, and privacy-preserving authentication in low-altitude flight applications.