Technique for designing differentially private algorithms
The exponential mechanism is a technique for designing differentially private algorithms. It was developed by Frank McSherry[1] and Kunal Talwar[2] in 2007. Their work was recognized as a co-winner of the 2009 PET Award for Outstanding Research in Privacy Enhancing Technologies.[3]
Most of the initial research in the field of differential privacy revolved around real-valued functions which have relatively low sensitivity to change in the data of a single individual and whose usefulness is not hampered by small additive perturbations. A natural question is what happens in the situation when one wants to preserve more general sets of properties. The exponential mechanism helps to extend the notion of differential privacy to address these issues. Moreover, it describes a class of mechanisms that includes all possible differentially private mechanisms.
^Frank McSherry
^Kunal Talwar
^"Past Winners of the PET Award".
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