Differential privacy (DP) is an approach for providing privacy while sharing information about a group of individuals, by describing the patterns within the group while withholding information about specific individuals.[1][2] This is done by making arbitrary small changes to individual data that do not change the statistics of interest. Thus the data cannot be used to infer much about any individual.
Another way to describe differential privacy is as a constraint on the algorithms used to publish aggregate information about a statistical database which limits the disclosure of private information of records in the database. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible even to internal analysts.
Roughly, an algorithm is differentially private if an observer seeing its output cannot tell whether a particular individual's information was used in the computation. Differential privacy is often discussed in the context of identifying individuals whose information may be in a database. Although it does not directly refer to identification and reidentification attacks, differentially private algorithms provably resist such attacks.[3]
^Hilton, M; Cal (2012). "Differential Privacy: A Historical Survey". Semantic Scholar. S2CID 16861132. Retrieved 31 December 2023.
^Dwork, Cynthia (2008-04-25). "Differential Privacy: A Survey of Results". In Agrawal, Manindra; Du, Dingzhu; Duan, Zhenhua; Li, Angsheng (eds.). Theory and Applications of Models of Computation. Lecture Notes in Computer Science. Vol. 4978. Springer Berlin Heidelberg. pp. 1–19. doi:10.1007/978-3-540-79228-4_1. ISBN 978-3-540-79227-7. S2CID 2887752.
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