We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge.

@misc{narayanan-2006, author = {Narayanan, Arvind and Shmatikov, Vitaly}, title = {How To Break Anonymity of the Netflix Prize Dataset}, year = {2006}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0610105}, keywords = {Preis, anonymity, dataset, netflix, prize, recommender}, abstract = { We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge.} }

%0 = misc %A = Narayanan, Arvind and Shmatikov, Vitaly %B = } %C = %D = 2006 %I = %T = How To Break Anonymity of the Netflix Prize Dataset} %U = http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0610105