Abstract
Differential privacy is rigorous framework for stating and enforcing privacy guarantees on computations over sensitive data. Informally, differential privacy ensures that the presence or absence of a single individual in a database has only a negligible statistical effect on the computation's result. Many specific algorithms have been proved differentially private, but manually checking that a given program is differentially private can be subtle, tedious, or both. This approach becomes unfeasible when larger programs are considered.
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