An Ad Omnia Approach to Defining and Achieving Private Data Analysis
Privacy, Security, and Trust in KDD"”PinKDD 2007 |
Published by Springer Verlag
2009 PET Award for Outstanding Research in Privacy Enhancing Technologies.
We briefly survey several privacy compromises in published datasets, some historical and some on paper. An inspection of these suggests that the problem lies with the nature of the privacy-motivated promises in question. These are typically syntactic, rather than semantic. They are also ad hoc , with insufficient argument that fulfilling these syntactic and ad hoc conditions yields anything like what most people would regard as privacy. We examine two comprehensive, or ad omnia, guarantees for privacy in statistical databases discussed in the literature, note that one is unachievable, and describe implementations of the other.