핵심 개념
提案されたVPASプロトコルは、データプライバシーを保護しながら入力検証と公開検証を実現することで、集計統計の効率的な処理を可能にします。
초록
Abstract introduces the importance of aggregate statistics in various domains like healthcare.
The work addresses challenges in input validation and public verifiability in privacy-preserving aggregation protocols.
VPAS protocol uses homomorphic encryption, Zero-Knowledge Proofs (ZKP), and blockchain for secure data processing.
Implementation of VPAS shows a 10x lower overhead compared to conventional zkSNARKs.
Application scenario discussed is Genome-Wide Association Studies (GWAS) for genomic data analysis.
Related works focus on privacy-preserving computation but lack input validation and public verifiability aspects.
System overview includes components like Collector, Clients, Aggregator, Auditor, and Distributed Ledger.
Security goals include Privacy, Robustness, Correctness, and Public Verifiability.
Threat model assumes potential compromise of all parties except one in the anytrust model.
통계
プロトコルは従来のzkSNARKsよりも10倍低いオーバーヘッドを示す結果が示されています。