Abstract
In all seriousness, Differential Privacy is a new technique and set of tools for managing responses to statistical queries over secured data, in such a way that the user cannot reconstruct more precise identification of principles in the dataset beyond a formally well-specified bound. This means that personally sensitive data such as Internet packet traces or social network measurements can be shared between researchers without invading personal privacy, and that assurances can be made with accuracy. With less seriousness, I would like to talk about Differential Piracy , but not without purpose. For sure, while there are legitimate reasons for upstanding citizens to live without fear of eternal surveillance, there is also a segment of society that gets away with things they shouldn't, under a cloak. Perhaps that is the (modest) price we have to pay for a modicum less paranoia in this brave new world. So, there has been a lot of work recently on Piracy Preserving Queries and Differential Piracy. These two related technologies exploit new ideas in statistical security. Rather than security through obscurity, the idea is to offer privacy through lack of differentiation (no, not inability to perform basic calculus, more the inability to distinguish between large numbers of very similar things).
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