Random additions efficiently anonymize large data sets

Balancing transparency and freedom of information with the right to privacy lays high demands on data handling methods. So far methods for anonymizing shared data sets have assumed that there is a distinction between details that can be used to identify an individual (quasi-identifiers) and details that are deemed 'sensitive' and private, but this is not always the case. Now Yuichi Sei and Akihiko Ohsuga from the University of Electro- Communications, alongside Takao Takenouchi from NEC Corporation in Japan, have devised an algorithm that efficiently anonymizes data sets without assuming this distinction.

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