@inproceedings{22b8d3196a9a48b3a97ae7b8ed2019cf,
title = "The composition theorem for differential privacy",
abstract = "Sequential querying of differentially private mechanisms degrades the overall privacy level. In this paper, we answer the fundamental question of characterizing the level of overall privacy degradation as a function of the number of queries and the privacy levels maintained by each privatization mechanism. Our solution is complete: we prove an upper bound on the overall privacy level and construct a sequence of privatization mechanisms that achieves this bound. The key innovation is the introduction of an operational interpretation of differential privacy (involving hypothesis testing) and the use of new data processing inequalities. Our result improves over the state-of-the-art and has immediate applications to several problems studied in the literature.",
author = "Peter Kairouz and Sewoong Oh and Pramod Viswanath",
note = "Publisher Copyright: Copyright {\textcopyright} 2015 by the author(s).; 32nd International Conference on Machine Learning, ICML 2015 ; Conference date: 06-07-2015 Through 11-07-2015",
year = "2015",
language = "English (US)",
series = "32nd International Conference on Machine Learning, ICML 2015",
publisher = "International Machine Learning Society (IMLS)",
pages = "1376--1385",
editor = "David Blei and Francis Bach",
booktitle = "32nd International Conference on Machine Learning, ICML 2015",
}