In the information realm, loss of privacy is usually associated with failure to control access to information, to control the \ufb02ow of information, or to control the purposes for which information is employed. Di\ufb00erential privacy arose in a context in which ensuring privacy is a challenge even if all these control problems are solved: privacy-preserving statistical analysis of data. The problem of statistical disclosure control \u2013 revealing accurate statistics about a set of respondents while preserving the privacy of individuals \u2013 has a venerable history, with an extensive literature spanning statistics, theoretical computer science, security, databases, and cryptography (see, for example, the excellent survey [1], the discussion of related work in [2] and the Journal of O\ufb03cial Statistics 9(2), dedicated to con\ufb01dentiality and disclosure control). This long history is a testament the importance of the problem. Statistical databases can be of enormous social value; they are used for apportioning resources, evaluating medical therapies, understanding the spread of disease, improving economic utility, and informing us about ourselves as a species. The data may be obtained in diverse ways. Some data, such as census, tax, and other sorts of o\ufb03cial data, are compelled; others are collected opportunistically, for example, from tra\ufb03c on the internet, transactions on Amazon, and search engine query logs; other data are provided altruistically, by respondents who hope that sharing their information will help others to avoid a speci\ufb01c misfortune, or more generally, to increase the public good. Altruistic data donors are typically promised their individual data will be kept con\ufb01dential \u2013 in short, they are promised \u201cprivacy.\u201d Similarly, medical data and legally compelled data, such as census data, tax return data, have legal privacy mandates. In our view, ethics demand that opportunistically obtained data should be treated no di\ufb00erently, especially when there is no reasonable alternative to engaging in the actions that generate the data in question. The problems remain: even if data encryption, key management, access control, and the motives of the data curator are all unimpeachable, what does it mean to preserve privacy, and how can it be accomplished?<\/p>\n","protected":false},"excerpt":{"rendered":"
In the information realm, loss of privacy is usually associated with failure to control access to information, to control the \ufb02ow of information, or to control the purposes for which information is employed. Di\ufb00erential privacy arose in a context in which ensuring privacy is a challenge even if all these control problems are solved: privacy-preserving […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561,13558],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-158702","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-security-privacy-cryptography","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, 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