An information-theoretic approach to privacy

By Lalitha Sankar, S. Rajagopalan, H. Vincent Poor. 2010

31 citations recorded by the source corpus.

Ensuring the usefulness of electronic data sources while providing necessary privacy guarantees is an important unsolved problem. This problem drives the need for an overarching analytical framework that can quantify the safety of personally identifiable information (privacy) while still providing a quantifable benefit (utility) to multiple legitimate information consumers. State of the art approaches have predominantly focused on privacy. This paper presents the first information-theoretic approach that promises an analytical model guaranteeing tight bounds of how much utility is possible for a given level of privacy and vice-versa.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Technical Privacy Metrics (2015, methodology): “For example, Bezzi [18] describes metrics from the data similarity category in terms of metrics from the uncertainty and information gain/loss categories, and SoriaComas and Domingo-Ferrer [121] showed that data similarity metrics can be related with metrics from the indistinguishability category.”
Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach (2011, methodology): “Our theorem in this context reported previously in [6] is presented with the full proof h ere.”
Minimax Filter: Learning to Preserve Privacy from Inference Attacks (2016, background): “Information-theoretic quantities were proposed by Sankar et al. (2010); Rebollo-Monedero et al. (2010); du Pin Calmon and Fawaz (2012) who analyzed privacy in terms of the rate-distortion theory in communication.”