If you were expecting anything more than Google trying to k-anonymize your datasets, you will be disappointed. “K-anonymity was born out of a desire both to qu

If you were expecting anything more than Google trying to k-anonymize your datasets, you will be disappointed. “K-anonymity was born out of a desire both to quantify the re-identifiability of a dataset, and to balance the usefulness of de-identified people data and the privacy of the people whose data is being used.” Researchers have also shown recently that many high-dimensional datasets (mobile phone metadata, online ratings, etc.) cannot be de-identified without loosing all their utility [1, 2, 3]. In that case, there is no distinction between quasi-identifiers and sensitive data. All the information in the dataset can be used to identify an individual. De-identification algorithms such as the one used here make strong assumptions that the data collector knows which information can and cannot be used to re-identify individuals. [1] https://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf https://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf [2] https://www.nature.com/articles/srep01376 https://www.nature.com/articles/srep01376 [3] http://science.sciencemag.org/content/347/6221/536.full http://science.sciencemag.org/content/347/6221/536.full