Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization

By Paul Ohm. 2009

1,090 citations recorded by the source corpus.

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “26,27 If data is archived or shared, it should be processed, not only to remove obvious personal identiﬁers, but also to prevent re-identiﬁcation via combinations of apparently non-sensitive attributes (Ohm, 2010).”
Using APIs for Data Collection on Social Media (2014, unknown): “… databases, researchers anonymize their datasets per default and test units are not publicly searchable, but as for instance discussed by Zimmer (2010) and Ohm (2010), it may be possible to de-‐ anonymize individuals, not only through publicized text strings,…”
Engineering Privacy by Design (2011, background): “If achieved, this would be a breakthrough in policy discourse comparable to the recently triggered discussions on the impossibility of database anonymization and the effects of these results on the concept of personal data [10, 38].”
Economic Analysis and Statistical Disclosure Limitation (2016, background): “Ohm (2010) argues there may be, for every individual, a “database of ruin” that can be constructed by linking together existing non-ruinous data.”
Wireless Sensor Networks for Healthcare (2010, background): “Quoting P. Ohm from a recent article: B Data can either be useful or perfectly anonymous, but never both [ [56].”
synthpop: Bespoke Creation of Synthetic Data in R (2016, unknown): “Advances in computer technology have shown that such measures may not prevent disclosure (Ohm 2010) and in addition they may compromise the conclusions one can draw from such data (Elliot and Purdam 2007; Winkler 2007).”
Twenty Security Considerations for Cloud-Supported Internet of Things (2016, unknown): “Benign sources and “anonymized” data may reveal little in isolation, but combining data from a number of sources can result in privacy-invading inference [9].”
Human-Data Interaction: The Human Face of the Data-Driven Society (2014, background): “Individuals hidden in previously anonymized, delinked personal data may become identiﬁable through application of newly open data sets (Ohm, 2010).”
An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence (2017, background): “The power of these inference techniques only grows as more data sets are added to the training base (Ohm, 2010).”
Differentially private sequential data publication via variable-length n-grams (2012, background): “Several sequential data sanitization algorithms have been presented recently [19], [3].”
Big data, bigger dilemmas: A critical review (2015, background): “…between the ethos of data sharing, liquidity, and transparency, on the one hand, and risks to privacy and anonymity through reidentification, on the other, emerges in such diverse areas as medicine, location-tagged payments, geo-locating mobile devices, and social media (Ohm, 2010; Tucker, 2013).”