PrivacyGroup Event:2016/03/04 Privacy as secure multiparty communication

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Private Data Analytics on Biomedical Sensing Data Via Distributed Computation

  • As Hong mentioned, perturbation techniques mean there is a trusted third party who holds all private data. This approach addresses that weakness with a technique that allows a third party to compute statistics on a private data set without learning anything beyond those statistics. The statistics themselves can be used to infer private data, however, so it is significant that this approach can be composed with a perturbation method (e.g. Dwork's Laplace mechnanism) to provide better privacy. It is significant that each data subject locally computes part of the statistics -- this is a massively distributed arrangement.

A brief primer on secure multiparty communication

Extra: Dwork at NIPS 2014 gives some useful explanations of results about Differential Privacy

  • Description of the definition itself is helpful, as is discussion of why DP algorithms are less likely to overfit to a hold out set