TR2017-167

Privacy-Utility Tradeoffs under Constrained Data Release Mechanisms


    •  Wang, Y., Basciftci, Y.O., Ishwar, P., "Privacy-Utility Tradeoffs under Constrained Data Release Mechanisms", arXiv, October 2017.
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      • @techreport{MERL_TR2017-167,
      • author = {Wang, Y. and Basciftci, Y.O. and Ishwar, P.},
      • title = {Privacy-Utility Tradeoffs under Constrained Data Release Mechanisms},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2017-167},
      • month = oct,
      • year = 2017,
      • url = {http://www.merl.com/publications/TR2017-167/}
      • }
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  • Research Areas:

    Multimedia, Information Security


Privacy-preserving data release mechanisms aim to simultaneously minimize information-leakage with respect to sensitive data and distortion with respect to useful data. Dependencies between sensitive and useful data results in a privacy-utility tradeoff that has strong connections to generalized rate-distortion problems. In this work, we study how the optimal privacy-utility tradeoff region is affected by constraints on the data that is directly available as input to the release mechanism. In particular, we consider the availability of only sensitive data, only useful data, and both (full data). We show that a general hierarchy holds: the tradeoff region given only the sensitive data is no larger than the region given only the useful data, which in turn is clearly no larger than the region given both sensitive and useful data. In addition, we determine conditions under which the tradeoff region given only the useful data coincides with that given full data. These are based on the common information between the sensitive and useful data. We establish these results for general families of privacy and utility measures that satisfy certain natural properties required of any reasonable measure of privacy or utility. We also uncover a new, subtler aspect of the data processing inequality for general non-symmetric privacy measures and discuss its operational relevance and implications. Finally, we derive exact closed-analyticform expressions for the privacy-utility tradeoffs for symmetrically dependent sensitive and useful data under mutual information and Hamming distortion as the respective privacy and utility measures.