Publications by Year: 2019

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.

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Aaron Fluitt, Aloni Cohen, Micah Altman, Kobbi Nissim, Salome Viljoen, and Alexandra Wood. 2019. “Data Protection's Composition Problem.” European Data Protection Law Review , 5, 3, Pp. 285-292. Publisher's Version EDPL 2019.pdf
Micah Altman, Stephen Chong, and Alexandra Wood. 2019. “Formalizing Privacy Laws for License Generation and Data Repository Decision Automation.” ArXiv. ArXiv VersionAbstract
In this paper, we summarize work-in-progress on expert system support to automate some data deposit and release decisions within a data repository, and to generate custom license agreements for those data transfers.
Our approach formalizes via a logic programming language the privacy-relevant aspects of laws, regulations, and best practices, supported by legal analysis documented in legal memoranda. This formalization enables automated reasoning about the conditions under which a repository can transfer data, through interrogation of users, and the application of formal rules to the facts obtained from users. The proposed system takes the specific conditions for a given data release and produces a custom data use agreement that accurately captures the relevant restrictions on data use. This enables appropriate decisions and accurate licenses, while removing the bottleneck of lawyer effort per data transfer. The operation of the system aims to be transparent, in the sense that administrators, lawyers, institutional review boards, and other interested parties can evaluate the legal reasoning and interpretation embodied in the formalization, and the specific rationale for a decision to accept or release a particular dataset.
ArXiv 2019 - 1910.10096.pdf
Anitha Gollamudi, Owen Arden, and Stephen Chong. 2019. “Information Flow Control for Distributed Trusted Execution Environments.” Computer Security Foundations. PDF