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Programming Language Techniques for Differential Privacy |
Marco Vassena |
EL41 |
2017/05/10 |
10:00-12:00 |
marco.pdf |
Data analysts mine large databases and crunch data in order to extrapolate statistics and interesting patterns. However, people's privacy is jeopardized in the process, whenever a database contains private data. *Differential privacy* has emerged recently as an appealing rigourous definition of privacy, which protects individuals in a database, while allowing data analysts to learn facts about the underling population, by adding noise to queries. Unfortunately, proving differential privacy of programs is a difficult and error-prone task. In this paper, we survey the state-of-the-art applications of programming languages techniques to develop principled approaches and tool support to ease the analysis and verification of probabilistic differential private programs. |
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