Jul 2026· Annual International Computer Software and Applications Conference· pp. 2729-2734· 0 citations· 14 references
Abstract
Zero-Knowledge Proofs (ZKP) provide strong data privacy and rely on the principle of proving a statement without revealing the underlying data. However, the ZK-SNARK system has a scalability problem while generating proofs. Systems like PLONK take longer to generate a proof with increasing size of the input data. EHR(Electronic Health Records) may present a short proving time but large size data sine when they contain images and genomics it could take more time to generate a proof. ZK-Rollup systems provide scalability by batching the transactions in off-chain mode; however, they also suffer from longer proving time with large data. In this paper, we propose an approach to generate KZG commitments from healthcare FHIR records and then use these values to generate single validity ZKP. In this way, we can not only reduce the proving time, but also ensure data integrity and data correctness.
Zero-knowledge machine learning (zkML) enables a server to perform verifiable inference while keeping model parameters private from the client. However, existing zkML systems incur prohibitive proof-generation costs. We observe that proof generation exhibits limited parallelism; that is, prover time does not decrease s...
Pawan Kumar Sanjaya, Christina Giannoula, Valdy Oktavian et al.· 0 citations
Existing Capture-the-Flag (CTF) platforms trust a single organizer, offer limited auditability, and are vulnerable to infrastructure-level manipulation. We propose zk–MPSFV, a zk-SNARK-based, multi-phase sub-flag verification scheme that replaces centralized scoring with an on-chain, zero-knowledge, publicly verifiable...
S. Khanji, Behzad Abdolmaleki, John A. Clark et al.· IEEE Computer Security Found...· 0 citations
PANDA is a scalable system that uses zero-knowledge proofs to prove the robustness and fairness properties of a model without revealing its private parameters, and can generate proofs of local robustness for neural networks with more than 2.9M parameters in 5 minutes, and can verify them in 10 seconds.
Youwei Zhong, Ben Merbaum, Timos Antonopoulos et al.· 0 citations
This SoK model federated learning as an append-only transcript of submissions, admissions, aggregation, and finalization events, and formalize verifiability as a collection of integrity claims issued by clients and the aggregator, and checked by different verifier classes.
Andrea Rizzini, Marco Esposito, Tommaso Gagliardoni et al.· Proceedings on Privacy Enhan...· 0 citations
A AegisPath is presented, it replaces centralized verification with distributed witness generation to protect AS-local configurations, decouples witness generation from repeated auditing through a commit-and-prove design, uses zero-knowledge proofs to answer queries without revealing forwarding paths, and supports incr...
Mingjun Fang, Shuhao Zheng, Zonglun Li et al.· Asia-Pacific Workshop on Net...· 0 citations
Zero-knowledge proofs (ZKPs) are increasingly used in blockchain systems, privacy-preserving applications, and verifiable cloud computation, but their practical deployment is still constrained by the cost of proof generation. This cost is especially important when proof generation is provided as a cloud-side service, w...
Weiliang Ma, Qian Xiong, Yu-Fan Bai et al.· Fall Joint Computer Conferen...· 0 citations
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