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Differential privacy is a meaningful and mathematically rigorous definition of privacy useful for quantifying and bounding privacy loss. Developed in the context of statistical disclosure control – providing accurate statistical information about a set of respondents while protecting the privacy of each individual – the concept applies more generally to any private data set for which it is desirable to release coarse-grained information while keeping private the details. Informally, differential privacy requires the probability distribution on the published results of an analysis to be “essentially the same,” independent of whether any individual opts in to or opts out of the data set. The probabilities are over the coin flips of the data analysis algorithm.
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Dwork, C. (2011). Differential Privacy. In: van Tilborg, H.C.A., Jajodia, S. (eds) Encyclopedia of Cryptography and Security. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5906-5_752
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DOI: https://doi.org/10.1007/978-1-4419-5906-5_752
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