Differentially Private Data Release under Partial Information

Monday, August 12, 2019 - 3:00 pm3:30 pm

David Zeber, Mozilla Corporation


Differential privacy (DP) is now a standard technique for releasing reports based on sensitive data. However, selecting and tuning a DP mechanism so as to obtain high utility of the privacy-protected data is often difficult without detailed knowledge of the characteristics of the sensitive dataset. We propose an applied methodology for guiding the implementation of DP data protections using only partial summary information about the private data. In this setting, candidate DP mechanisms can be evaluated across possible realizations of the sensitive dataset, the selection of which is feasibly constrained using the available partial information. We demonstrate our approach for the problem of reporting the DP-protected distribution of item frequencies from a dataset of user-item pairs.

David Zeber, Mozilla Corporation

David Zeber is a research engineer at Mozilla. Reaching across data science, machine learning and differential privacy, his work focuses on collecting and modeling user data in a privacy-preserving way to improve user experience in the Firefox browser and on the Web.

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@inproceedings {238172,
author = {David Zeber},
title = {Differentially Private Data Release under Partial Information},
booktitle = {2019 {USENIX} Conference on Privacy Engineering Practice and Respect ({PEPR} 19)},
year = {2019},
address = {Santa Clara, CA},
url = {https://www.usenix.org/node/238173},
publisher = {USENIX Association},
month = aug