Differentially Private Set-Valued Data Release Against Incremental Updates

Provided by: Springer Healthcare
Topic: Big Data
Format: PDF
Publication of the private set-valued data will provide enormous opportunities for counting queries and various data mining tasks. Compared to those previous methods based on partition-based privacy models (e.g., k-anonymity), differential privacy provides strong privacy guarantees against adversaries with arbitrary background knowledge. However, the existing solutions based on differential privacy for data publication are currently limited to static datasets, and do not adequately address today's demand for up-to-date information. In this paper, the author's addresses the problem of differentially private set-valued data release on an incremental scenario in which the data need to be transformed are not static.

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