Universiteit Leiden

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Secure and Efficient Computing on Private Data

  • Eleftheria Makri, Saxion University of Applied Sciences and KU Leuven
Thursday 2 June 2022
Niels Bohrweg 1
2333 CA Leiden
room 402

In this talk, we summarize the main results of my PhD thesis, and discuss directions for future work. The overarching technique that has been the basis of all contributions in this thesis is Multiparty Computation (MPC). MPC is a cryptographic primitive that allows a set of mutually distrusting parties to compute a function over their private inputs, without revealing to each other or to outside observers these inputs. MPC research today aims at combating the inherent tradeoffs among efficiency, security, and expressiveness of the computable functions, and combine the best of all three worlds either for a specific cryptographic primitive, or for a particular application scenario. We focus on three concrete application scenarios, namely (1) Privacy-Preserving Genome-Wide Association Studies; (2) Private Image Classification; and (3) Secure RSA Modulus Generation, and discuss our contributions. Then, we elaborate on how we improved two MPC building blocks (or primitives): (1) Multiparty Arithmetic Garbling, and concretely Selector Gates; and (2) Secure Comparison protocols. 

About Eleftheria Makri

I am an Information Security professional, specialized in Cryptography, and my career is focused on research and education. I work as a lecturer/researcher at Saxion University of Applied Sciences, the Netherlands, where I am affiliated with the Bachelor programme Security Management (August 2014 - present), and the research group Social Safety & Security -lectoraat Maatschappelijke Veiligheid- (February 2022 - present). As a Saxion lecturer, I integrate theory and practice to disseminate the information security specialist's key professional aspirations. Furthermore, I am a free researcher at COSIC, KU Leuven, Belgium (July 2021 - present). At COSIC, I do research on computing on encrypted data, based on the techniques of Homomorphic Encryption, and Multiparty Computation. My research interests lie mainly in the field of Applied Cryptography, and Privacy Enhancing Technologies. Specifically, I am interested in technologies and applications of Computing on Encrypted Data (e.g., homomorphic encryption, and multiparty computation).

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