Conference Proceedings

On Properties and Optimization of Information-theoretic Privacy Watchdog

Parastoo Sadeghi, Ni Ding, Thierry Rakotoarivelo

IEEE Information Theory Workshop (ITW) | IEE | Published : 2021


We study the problem of privacy preservation in data sharing, where S is a sensitive variable to be protected and X is a non-sensitive useful variable correlated with S. Variable X is randomized into variable Y , which will be shared or released according to pY |X(y|x). We measure privacy leakage by information privacy (also known as log-lift in the literature), which guarantees mutual information privacy and differential privacy (DP). Let X∈c ⊆ X contain elements in the alphabet of X for which the absolute value of log-lift (abs-log-lift for short) is greater than a desired threshold ∈. When elements x ∈ X∈c are randomized into y ∈ Y, we derive the best upper bound on the abs-log-lift acros..

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University of Melbourne Researchers


Awarded by ARC Future Fellowship

Funding Acknowledgements

Her work is partly supported by the Data61 CRP: IT-PPUB and by the ARC Future Fellowship FT190100429.

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