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Cited by 304

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Tracing traitors

Chor, B., Fiat, A., Naor, M., Pinkas, B.·2000·IEEE Transactions on Information Theory

Abstract

We give cryptographic schemes that help trace the source of leaks when sensitive or proprietary data is made available to a large set of parties. A very relevant application is in the context of pay television, where only paying customers should be able to view certain programs. In this application, the programs are normally encrypted, and then the sensitive data is the decryption keys that are given to paying customers. If a pirate decoder is found, it is desirable to reveal the source of its decryption keys. We describe fully resilient schemes which can be used against any decoder which decrypts with nonnegligible probability. Since there is typically little demand for decoders which decrypt only a small fraction of the transmissions (even if it is nonnegligible), we further introduce threshold tracing schemes which can only be used against decoders which succeed in decryption with probability greater than some threshold. Threshold schemes are considerably more efficient than fully resilient schemes.

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Metadata

Authors
Chor, B., Fiat, A., Naor, M., & Pinkas, B.
Venue
IEEE Transactions on Information Theory
Published
2000-05
Volume
46
Issue
3
Pages
893-910
ISSN
0018-9448 (ISSN-L)
OpenAlex
W2295616646
Cited by
304
References
15
FWCI
14.16
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Tracing traitors. https://app.cochat.ai/papers/10.1109/18.841169
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Chor, B., Fiat, A., Naor, M., & Pinkas, B. (2000). Tracing traitors. IEEE Transactions on Information Theory. https://doi.org/10.1109/18.841169

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.