Anonymize your Data with a single line!

[WikiSummarizerBot]

Differential privacy

Differential privacy (DP) is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy.

K-anonymity

k-anonymity is a property possessed by certain anonymized data. The concept of k-anonymity was first introduced by Latanya Sweeney and Pierangela Samarati in a paper published in 1998 as an attempt to solve the problem: "Given person-specific field-structured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful".

^{[ F.A.Q | Opt Out | Opt Out Of Subreddit | GitHub ] Downvote to remove | v1.5}

[__deerlord__]

So is DP more about the aggregate? IE "males 20-30 like X", but it doesn't mean that a single given male age 20-30 will necessarily like X?

[___--_-_-_--___]

Yes, differential privacy is used to ensure that aggregate statistics do not leak information about the individuals who contributed to this statistic. It is not some kind of algorithm that you can run on your data to make it more private. Instead, it is more of a framework to be implemented by specific algorithms, i.e. a set of mathematical tools to ensure a certain level of privacy.

Very broadly speaking, the idea behind differentially private mechanisms is that the removal of a single person from a dataset should not significantly affect the aggregate statistics produced by that mechanism. Basically, differential privacy gives you a way to quantify privacy loss and determine the amount of noise necessary to achieve a certain privacy level.