Publications by Year: 2019

Kobbi Nissim and Uri Stemmer. 3/2019. “Concentration Bounds for High Sensitivity Functions Through Differential Privacy.” Journal of Privacy and Confidentiality, 9, 1. arXiv PageAbstract

A new line of work [6, 9, 15, 2] demonstrates how differential privacy [8] can be used as a mathematical tool for guaranteeing generalization in adaptive data analysis. Specifically, if a differentially private analysis is applied on a sample S of i.i.d. examples to select a lowsensitivity function f , then w.h.p. f (S) is close to its expectation, although f is being chosen based on the data. Very recently, Steinke and Ullman [16] observed that these generalization guarantees can be used for proving concentration bounds in the non-adaptive setting, where the low-sensitivity function is fixed beforehand. In particular, they obtain alternative proofs for classical concentration bounds for low-sensitivity functions, such as the Chernoff bound and McDiarmid’s Inequality. In this work, we set out to examine the situation for functions with high-sensitivity, for which differential privacy does not imply generalization guarantees under adaptive analysis. We show that differential privacy can be used to prove concentration bounds for such functions in the non-adaptive setting.

Anitha Gollamudi, Owen Arden, and Stephen Chong. 2019. “Information Flow Control for Distributed Trusted Execution Environments.” Computer Security Foundations. PDF