Publication Date:
2016-03-19
Description:
Sanchez et al.'s textbook k-anonymization example does not prove, or even suggest, that location and other big-data data sets can be anonymized and of general use. The synthetic data set that they "successfully anonymize" bears no resemblance to modern high-dimensional data sets on which their methods fail. Moving forward, deidentification should not be considered a useful basis for policy.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉de Montjoye, Yves-Alexandre -- Pentland, Alex Sandy -- New York, N.Y. -- Science. 2016 Mar 18;351(6279):1274. doi: 10.1126/science.aaf1578.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Harvard University, Institute for Quantitative Social Science, Cambridge, MA 02138, USA. yvesalexandre@demontjoye.com. ; Massachusetts Institute of Technology, Cambridge, MA 02139, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26989244" target="_blank"〉PubMed〈/a〉
Keywords:
*Commerce
;
*Data Collection
;
Female
;
Humans
;
*Information Dissemination
;
Male
;
*Privacy
Print ISSN:
0036-8075
Electronic ISSN:
1095-9203
Topics:
Biology
,
Chemistry and Pharmacology
,
Computer Science
,
Medicine
,
Natural Sciences in General
,
Physics
