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ICFP
2010
ACM

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

13 years 4 months ago
Distance makes the types grow stronger: a calculus for differential privacy
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 estab...
Jason Reed, Benjamin C. Pierce
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICFP
Authors Jason Reed, Benjamin C. Pierce
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