Sirvi Autor "Talviste, Riivo, juhendaja" järgi

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Parallel and Cloud-Native Secure Multi-Party Computation
(Tartu Ülikool, 2022) Tali, Kert; Talviste, Riivo, juhendaja; Jakovits, Pelle, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Secure multi-party computation (MPC) enables analysis based on sensitive data from multiple data owners, applying distributed cryptographic protocols to ensure privacy. Such protocols introduce distinct communication requirements, causing the computation to run significantly longer than its counterpart, conventional computing. General MPC frameworks are available that make it simple to develop such privacy-preserving applications, but running said applications assumes multiple non-colluding computing parties that host the protocol runtimes, having rigorously set up the required infrastructure. Utilising cloud resources for this occasion is a good alternative to on-premises deployments. First, it allows for a larger degree of automation in the infrastructure set-up. Secondly, cloud datacenters enjoy superior network characteristics, detrimental for MPC performance, and offer elastic compute resources at competitive price models. This thesis presents a cloud-native deployment of the SHAREMIND MPC framework on Kubernetes. It further proposes methods for parallel programming, with which MPC applications could be scaled over clusters. Familiar programming models, MapReduce and bulk-synchronous parallel, are adapted to MPC, and benchmarked in commodity clouds, showing near-linear speedup.
Privacy Preserving Fingerprint Identification
(Tartu Ülikool, 2020) Eerikson, Hendrik; Talviste, Riivo, juhendaja; Krips, Kristjan, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Privacy preserving technologies are used to create applications for computing on sensitive data without compromising on the secrecy of said data. In this thesis, secret sharing based multi-party computation is used to identify a fingerprint sample amongst a database of templates while preserving the secrecy of the sample and the templates. The FingerCode representation of fingerprints is used. Privacy preserving fingerprint identification mitigates some of the privacy and security risks in biometric identification systems. The secret sharing based fingerprint identification application developed in this thesis is more performant than a previous homomorphic encryption based one. Methodology for identifying fingerprints and programming privacy preserving applications using multi-party computation is given. Fingerprint-based identification systems are vital tools for border control and law enforcement. Privacy preserving fingerprint identification could be used to prevent leakage and abuse of fingerprint data.