Welcome to the resource topic for 2012/456
Title:
Crowd-Blending Privacy
Authors: Johannes Gehrke, Michael Hay, Edward Lui, Rafael Pass
Abstract:We introduce a new definition of privacy called \emph{crowd-blending privacy} that strictly relaxes the notion of differential privacy. Roughly speaking, k-crowd blending private sanitization of a database requires that each individual i in the database blends'' with $k$ other individuals $j$ in the database, in the sense that the output of the sanitizer is indistinguishable’’ if i‘s data is replaced by j‘s. We demonstrate crowd-blending private mechanisms for histograms and for releasing synthetic data points, achieving strictly better utility than what is possible using differentially private mechanisms. Additionally, we demonstrate that if a crowd-blending private mechanism is combined with a ``pre-sampling’’ step, where the individuals in the database are randomly drawn from some underlying population (as is often the case during data collection), then the combined mechanism satisfies not only differential privacy, but also the stronger notion of zero-knowledge privacy. This holds even if the pre-sampling is slightly biased and an adversary knows whether certain individuals were sampled or not. Taken together, our results yield a practical approach for collecting and privately releasing data while ensuring higher utility than previous approaches.
ePrint: https://eprint.iacr.org/2012/456
Talk: https://www.youtube.com/watch?v=NxnEbP94rLk
Slides: https://iacr.org/cryptodb/archive/2012/CRYPTO/presentation/8-1-Lui.pdf
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