Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Differential privacy
Summary
Differential privacy (DP) is an approach to providing privacy while sharing information about a group of individuals, by describing the patterns within the group while withholding information about specific individuals. 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.
Differential privacy was developed by cryptographers and thus is often associated with cryptography, and draws much of its language from cryptography.
Official statistics organizations are charged with collecting information from individuals or establishments, and publishing aggregate data to serve the public interest. For example, the 1790 United States Census collected information about individuals living in the United States and published tabulations based on sex, age, race, and condition of servitude.
Statistical organizations have long collected information under a promise of confidentiality that the information provided will be used for statistical purposes, but that the publications will not produce information that can be traced back to a specific individual or establishment.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.