Paper 2023/1377

Janus: Fast Privacy-Preserving Data Provenance For TLS

Abstract

Web users can gather data from secure endpoints and demonstrate the provenance of sensitive data to any third party by using privacy-preserving TLS oracles. In practice, privacy-preserving TLS oracles remain limited and cannot selectively verify larger sensitive data sets. In this work, we introduce a new oracle protocol, which reaches new scales in selectively verifying the provenance of confidential web data. The novelty of our work is a construction which deploys an honest verifier zero-knowledge proof system in the asymmetric privacy setting while retaining security against malicious adversaries. Concerning TLS 1.3, we optimize the garble-then-prove paradigm in a security setting with malicious adversaries. Here, we show that a specific operation mode of TLS 1.3 allows to use semi-honest secure computations without authentic garbling for the majority of computations in the garble phase. Our performance improvements reach new efficiency scales in verifying private data provenance and facilitate the practical deployment of privacy-preserving TLS oracles in web browsers.