Andreas Klinger: Privacy Preserving Online Algorithms



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Secure multi-party computation (SMPC) currently allows a fixed number of parties that know each other a priori to compute a function over their private inputs in a distributed fashion in a way such that each party only learns its prescribed output and anything it can deduce from combining its prescribed output with its own private input. Andreas Klinger’s research focuses on relaxing two central assumptions of current SMPC protocols and devising new security definitions in these settings. The first assumption is that the number of parties that want two securely compute a function is a priori fixed and does not change over time. The second assumption is that these parties know each other a priori. Relaxing the first assumption and devising novel security notations in this setting will allow for proofing security properties of privacy-preserving online-functionalities implementing the distributed computation of online algorithms. Here, Klinger already introduced several new flavours of online trusted third parties and analysed in how far these trusted third parties can be simulated by distributed protocols. Relaxing the second assumption will enable anonymous SMPC and thus enable another new class of applications to be provided in a privacy-preserving fashion.