Explore a groundbreaking approach to transparent and privacy-preserving data services in this conference talk from USENIX Security '23. Delve into the innovative TAP system, which addresses the limitations of existing authenticated data structures by introducing a novel tree structure that supports efficient result verification while maintaining user data privacy. Learn how TAP utilizes independent audits with zero-knowledge range proofs to ensure correct tree construction without compromising sensitive information. Discover the system's capability to support a wide range of verifiable operations, including quantiles and sample standard deviations, while achieving practical performance at scale. Compare TAP's effectiveness against state-of-the-art baselines like IntegriDB and Merkle2, and gain insights into the future of secure, transparent, and privacy-preserving multi-user data services.
TAP - Transparent and Privacy-Preserving Data Services
Overview
Syllabus
USENIX Security '23 - TAP: Transparent and Privacy-Preserving Data Services
Taught by
USENIX
Related Courses
-
Prime Match - A Privacy-Preserving Inventory Matching System
-
SecretFlow-SPU - A Performant and User-Friendly Framework for Privacy-Preserving Machine Learning
-
On - The Lack Of - Location Privacy in Crowdsourcing Applications
-
FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via Federated Multi-Modal Contrastive Learning
-
BITE - Bitcoin Lightweight Client Privacy Using Trusted Execution
-
Fine-Grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation
