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Cited by 935

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

Direct anonymous attestation

Brickell, Ernie, Camenisch, Jan, Chen, Liqun·2004·Proceedings of the 11th ACM conference on Computer and communications security

Abstract

This paper describes the direct anonymous attestation scheme (DAA). This scheme was adopted by the Trusted Computing Group (TCG) as the method for remote authentication of a hardware module, called Trusted Platform Module (TPM), while preserving the privacy of the user of the platform that contains the module. DAA can be seen as a group signature without the feature that a signature can be opened, i.e., the anonymity is not revocable. Moreover, DAA allows for pseudonyms, i.e., for each signature a user (in agreement with the recipient of the signature) can decide whether or not the signature should be linkable to another signature. DAA furthermore allows for detection of "known" keys: if the DAA secret keys are extracted from a TPM and published, a verifier can detect that a signature was produced using these secret keys. The scheme is provably secure in the random oracle model under the strong RSA and the decisional Diffie-Hellman assumption.

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Metadata

Authors
Brickell, E., Camenisch, J., & Chen, L.
Venue
Proceedings of the 11th ACM conference on Computer and communications security
Publisher
ACM
Published
2004-10-25
Pages
132-145
ISSN
1543-7221 (ISSN-L)
OpenAlex
W1978183200
Cited by
935
References
29
FWCI
21.90
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Direct anonymous attestation. https://app.cochat.ai/papers/10.1145/1030083.1030103
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Brickell, E., Camenisch, J., & Chen, L. (2004). Direct anonymous attestation. Proceedings of the 11th ACM conference on Computer and communications security. https://doi.org/10.1145/1030083.1030103

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.