NDLI: Distance makes the types grow stronger:a calculus for differential privacy

Content Provider	ACM Digital Library
Author	Pierce, Benjamin C. Reed, Jason
Abstract	We want assurances that sensitive information will not be disclosed when aggregate data derived from a database is published. Differential privacy offers a strong statistical guarantee that the effect of the presence of any individual in a database will be negligible, even when an adversary has auxiliary knowledge. Much of the prior work in this area consists of proving algorithms to be differentially private one at a time; we propose to streamline this process with a functional language whose type system automatically guarantees differential privacy, allowing the programmer to write complex privacy-safe query programs in a flexible and compositional way. The key novelty is the way our type system captures function sensitivity, a measure of how much a function can magnify the distance between similar inputs: well-typed programs not only can't go wrong, they can't go too far on nearby inputs. Moreover, by introducing a monad for random computations, we can show that the established definition of differential privacy falls out naturally as a special case of this soundness principle. We develop examples including known differentially private algorithms, privacy-aware variants of standard functional programming idioms, and compositionality principles for differential privacy.
Starting Page	157
Ending Page	168
Page Count	12
File Format	MOBI / PRC EPUB PDF
ISSN	03621340 15581160
DOI	10.1145/1932681.1863568
Journal	ACM SIGPLAN Notices (SIGP)
Volume Number	45
Issue Number	9
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1983-05-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Differential privacy Type systems
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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