A programming approach to differential privacy

  • Alejandro Russo. Chalmers University of Technology, Suecia.
    Marco Gaboardi. University of Boston, Estados Unidos.
  • Track de Fundamentos de Lenguajes de Programación.

Nota: El curso será dictado parte en español y parte en inglés

Resumen

Differential Privacy offers ways to answer statistical queries about sensitive data while providing strong provable privacy guarantees ensuring that the presence or absence of a single individual in the data has a negligible statistical effect on the query’s result. Differential privacy is becoming a gold standard in data privacy and it is now used by statistical agencies like the US Census Bureau and companies like Apple, Google, and Uber. This course will focus on fundamental results about the theory and practice of differential privacy—some of them being based on latest research results of my authorship. The course will introduce students to some fundamental mechanisms for building differentially private queries. The course will also present a novel framework for programming such queries and how to use them to design privacy-preserving applications that take into account not only the privacy of individuals but also the accuracy of results.

Temario

• Formalization of data privacy • Differential privacy and its properties • Basic algorithms for differential privacy and their accuracy • Combining different building blocks • Other models for differential privacy.

Prerrequisitos

Basic programming skills and basic understanding of probabilities.

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