Welcome to the resource topic for 2023/171
Title:
On Differential Privacy and Adaptive Data Analysis with Bounded Space
Authors: Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou
Abstract:We study the space complexity of the two related fields of differential privacy and adaptive data analysis. Specifically,
(1) Under standard cryptographic assumptions, we show that there exists a problem P that requires exponentially more space to be solved efficiently with differential privacy, compared to the space needed without privacy. To the best of our knowledge, this is the first separation between the space complexity of private and non-private algorithms.
(2) The line of work on adaptive data analysis focuses on understanding the number of samples needed for answering a sequence of adaptive queries. We revisit previous lower bounds at a foundational level, and show that they are a consequence of a space bottleneck rather than a sampling bottleneck.
To obtain our results, we define and construct an encryption scheme with multiple keys that is built to withstand a limited amount of key leakage in a very particular way.
ePrint: https://eprint.iacr.org/2023/171
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .