Organizer: Giorgia Azzurra Marson / Nina Bindel
In 2009, Gentry presented the first Fully Homomorphic Encryption (FHE) scheme, which intuitively supports arbitrary computation over encrypted data without the need to decrypt. While, theoretically, FHE schemes can be used in many settings to provide data privacy (in terms of data confidentiality), in practice they suffer from severe efficiency drawbacks. Somewhat Homomorphic Encryption (SWHE), on the other hand, constitutes a relaxed form of FHE which only supports the computation of a limited set of functions on encrypted data (i.e., functions that can be represented as an arithmetic circuit with fixed and restricted amounts of additions and multiplications).
In this talk, I would like to show that the limited functionality of SWHE schemes can be sufficient to provide data privacy in specific application scenarios and can lead to constructions efficient enough for practical use. Concretely, I will look at two example applications from different domains: (1) enhancing privacy in recommender systems based on social networks and (2) privately outsourcing forensic image recognition. Using the different characteristics of these settings, I will identify some features of SWHE which make the further study of this type of encryption within the area of Privacy-Enhancing Technologies particularly worthwhile.
Andreas Peter graduated with a M.Sc. in mathematics at both the University of Cambridge (UK) and the University of Oldenburg (Germany) in 2008 and 2009, respectively. Subsequently, he received the Ph.D. in computer science from the Technical University of Darmstadt (Germany) in 2013. His Ph.D. thesis deals with the topic of secure outsourcing of computation with a special focus on homomorphic encryption. Since 2014, he is employed as an assistant professor in the group on Services, Cybersecurity and Safety at the University of Twente. His research lies in the area of security and privacy in IT systems. In particular, he is focusing on both fundamental and applied security and privacy aspects in cloud computing, critical infrastructure protection, participatory sensing, biometrics, digital forensics, and eHealth.