https://dasya.itu.dk/tags/theoretical-computer-science/ Recent content in Theoretical Computer Science on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Mon, 15 Mar 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mpc-databases/ Mon, 15 Mar 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mpc-databases/ (This topic is going to be co-supervised by Bernardo Machado David [http://www.bmdavid.com/]) Database systems managing private data may leak sensitive information when queries are done in the clear, even if the data itself is encrypted. A recent line of research has looked into combining database engines supporting standard SQL queries with techniques for secure Multiparty Computation (MPC), which allow for both queries and data to be handled privately. In these hybrid systems, not only the data is protected but the queries made by legitimate users are kept private even from the database engine itself (and consequently from the systems executing this engine). https://dasya.itu.dk/for-students/proposals/archive/privacypreserving/ Tue, 08 Dec 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/privacypreserving/ Given a private database that I can access only through specific queries, there is still a lot I can learn on its entries [1]. Differential Privacy (DP) tackles this: letting me learn the (approximate) result of complex queries on a database, but preventing me from learning much about its specific entries. The basic approach of DP often boils down to: “apply a privacy-preserving transformation T on the result of the query F or on the data itself before computing F”.