Details

Autor(en) / Beteiligte

• Kumar, Vireshwar
• Li, He
• Luther, Noah
• Asokan, Pranav
• Park, Jung-Min (Jerry)
• Bian, Kaigui
• Weiss, Martin B. H.
• Znati, Taieb

Titel

Direct Anonymous Attestation with Efficient Verifier-Local Revocation for Subscription System

Ist Teil von

• Proceedings of the 2018 on Asia Conference on Computer and Communications Security, 2018, p.567-574

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2018

Quelle

ACM Digital Library

Beschreibungen/Notizen

• For a computing platform that is compliant with the Trusted Platform Module (TPM) standard, direct anonymous attestation (DAA) is an appropriate cryptographic protocol for realizing an anonymous subscription system. This approach takes advantage of a cryptographic key that is securely embedded in the platform's hardware, and enables privacy-preserving authentication of the platform. In all of the existing DAA schemes, the platform suffers from significant computational and communication costs that increase proportionally to the size of the revocation list. This drawback renders the existing schemes to be impractical when the size of the revocation list grows beyond a relatively modest size. In this paper, we propose a novel scheme called Lightweight Anonymous Subscription with Efficient Revocation (LASER) that addresses this very problem. In LASER, the computational and communication costs of the platform's signature are multiple orders of magnitude lower than the prior art. LASER achieves this significant performance improvement by shifting most of the computational and communication costs from the DAA's online procedure (i.e., signature generation) to its offline procedure (i.e., acquisition of keys/credentials). We have conducted a thorough analysis of LASER's performance related features. We have implemented LASER on a laptop with an on-board TPM. To the best of our knowledge, this is the first implementation of a DAA scheme on an actual TPM cryptoprocessor that is compliant with the most recent TPM specification, viz., TPM 2.0.