PhD defense Ferran Alborch Escobar: Private Data Analysis over Encrypted Databases: Mixing Functional Encryption with Computational Differential Privacy
Télécom Paris, 19 place Marguerite Perey F-91120 Palaiseau [getting there], amphi 2 and in videoconferencing
Jury
- Céline Chevalier, Associate Professor, Université Panthéon-Assas Paris 2, France (Reviewer)
- Javier Herranz Sotoca, Associate Professor, Universitat Politècnica de Catalunya, Spain (Reviewer)
- Melek Önen, Professor, Eurecom, France (Examiner)
- Dario Catalano, Professor, Università di Catania, Italy (Examiner)
- Jacques Traoré, Research Enginrer, Orange Innovation, France (Examiner)
- Duong Hieu Phan, Professor, Télécom Paris, France (Thesis director)
- Fabien Laguillaumie, Professor, Université de Montpellier, France (Thesis co-supervisor)
- Sébastien Canard, Professor, Télécom Paris, France (Thesis co-director)
Abstract
In our current digitalized society, data is ruling the world. But as it is most of the time related to individuals, its exploitation should respect the privacy of the latter. This issue has raised the differential privacy paradigm, which permits to protect individuals when querying databases containing data about them. But with the emergence of cloud computing, it is becoming increasingly necessary to also consider the confidentiality of « on-cloud » storage confidentiality of such vast databases, using encryption techniques. This thesis studies how to provide both privacy and confidentiality of such outsourced databases by mixing two primitives: computational differential privacy and functional encryption.
… between computational differential privacy and functional encryption for randomized functions in a generic way. We analyze the privacy of the setting where a malicious analyst may access the encrypted data stored in a server, either by corrupting or breaching it, and prove that a secure randomized functional encryption scheme supporting the appropriate family of functions guarantees the computational differential privacy of the system.
Second, we construct efficient randomized functional encryption schemes for certain useful families of functions, and we prove them secure in the standard model under well-known assumptions. The families of functions considered are linear functions, used for example in counting queries, histograms and linear regressions, and quadratic functions, used for example in quadratic regressions and hypothesis testing. The schemes built are then used together with the first result to construct encrypted databases for their corresponding family of queries.
Finally, we implement both randomized functional encryption schemes to analyze their efficiency. This shows that our constructions are practical for databases with up to 1 000 000 entries in the case of linear queries and databases with up to 10 000 database entries in the case of quadratic queries.