« Personal data is the new oil of the internet and the new currency of the digital world. » claimed Meglena Kouneva, European Commissioner for Consumer Protection in March 2009. The value of personal data for industry, science and society in general is widely recognized today. However, the potentially identifying and sensitive nature of personal data is often a major obstacle to their collection, processing, and sharing. The goal of privacy-preserving techniques in data-centric systems is precisely to enable a wide diversity of usages of big personal data while still providing strong privacy guarantees. The objective of this course is to give to students a hands-on introduction to the main issues faced by these techniques and to the diversity of solutions found for reaching valuable privacy/utility tradeoffs in real life.
After an introduction to the privacy issues of personal data in real-life, we will focus on privacy-preserving techniques in data-centric systems and study technical answers to the questions of sharing, querying, and analyzing personal data while still providing sound privacy guarantees. Our study will include formal protection models (e.g., differential privacy, computational indistinguishability), the algorithms for supporting the targetted usages while satisfying the chosen model (e.g., based on random perturbations, on homomorphic encryption), and their limits (e.g., attacks, costs). We will also provide to students a short introduction to the privacy laws (e.g., GDPR, privacy impact assessment).
Privacy, partial information leakage, differential privacy, encryption, personal data.
Basics in the Python programing language, basic knowledge in cryptography, basic skills in statistics and probabilities.
- Privacy-Preserving Data Publishing by Bee-Chung Chen, Daniel Kifer, Kristen LeFevre and Ashwin Machanavajjhala, 2009.
- The Algorithmic Foundations of Differential Privacy, by Cynthia Dwork and Aaron Roth, 2014.
- Foundations of Cryptography: Volume 2, Basic Applications, by Oded Goldreich, 2004
Tristan Allard (responsible and main teacher) and teaching assistants for the technical part of the course, and external experts for the legal part.
“I am an associate professor (“maître de conférences”) since September 2014 at the Univ Rennes, CNRS, Irisa. I am also a fixed term (March 2020 to March 2023) associate professor (“professeur associé”) at the Université du Québec à Montréal (for co-supervising a joint PhD thesis between UQAM and UR1). Before that, I was a postdoctoral researcher at the Inria Zenith team in Montpellier. I conducted my Ph.D. thesis in Computer Science in the Inria SMIS team and received it from the University of Versailles in December 2011. The volume, variety, and velocity of digital personal data are increasing at a fast pace. Enabling both daily uses and large-scale analysis of personal data while preserving individuals’ privacy is a key challenge in building a knowledge society. My research interests lie within this wide field. I am particularly interested in the combination of differential privacy with cryptography (privacy-preserving data querying, privacy-preserving crowdsourcing, privacy-preserving data mining). And recently I got diverted by the study of browser fingerprints for web authentication.”