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The Strange Case of Privacy in Equilibrium Models

Presented by: 
Mallesh Pai
Friday 28th October 2016 - 16:00 to 17:00
INI Seminar Room 1
Joint work with Rachel Cummings, Katrina Ligett and Aaron Roth

The literature on differential privacy by and large takes the data set being being analyzed as exogenously given. As a result, by varying a privacy parameter in his algorithm, the analyst straightforwardly chooses the potential privacy loss of any single entry in the data set.  Motivated by privacy concerns on the internet, we consider a stylized setting where the dataset is endogenously generated, depending on the privacy parameter chosen by the analyst. In our model, an agent chooses whether to purchase a product. This purchase decision is recorded, and a differentially private version of his purchase decision may be used by an advertiser to target the consumer. A change in the privacy parameter therefore affects, in equilibrium, the agents' purchase decision, the price of the product, and the targeting rule used by the advertiser. We demonstrate that the comparative statics with respect to privacy parameter may be exactly reversed relative to the exogenous data set benchmark, for example a higher privacy parameter may nevertheless be more informative etc.  More care is needed in understanding the effects of private analysis of a data set that is endogenously generated.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons