A systematic review of re-identification attacks on health data

• Published in: PloS one 2011-12, Vol.6 (12), p.e28071-e28071
• Main Authors: El Emam, Khaled, Jonker, Elizabeth, Arbuckle, Luk, Malin, Bradley
• Format: Article
• Language: English
• Subjects: Computer Science; Computer Security; Confidence intervals; Confidentiality - legislation & jurisprudence; Consent; Data integrity; Databases, Factual; Digital libraries; Digital systems; Electronic health records; Genomes; Group insurance; Health; Health Insurance Portability and Accountability Act; Humans; Identification; Identification methods; Informatics; Laboratories; Legislation; Medical Records Systems, Computerized; Medical research; Medicine; Models, Statistical; Patients; Privacy; Reproducibility of Results; Science Policy; Social and Behavioral Sciences; Social networks; Software; Studies; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0028071

Privacy legislation in most jurisdictions allows the disclosure of health data for secondary purposes without patient consent if it is de-identified. Some recent articles in the medical, legal, and computer science literature have argued that de-identification methods do not provide sufficient protection because they are easy to reverse. Should this be the case, it would have significant and important implications on how health information is disclosed, including: (a) potentially limiting its availability for secondary purposes such as research, and (b) resulting in more identifiable health information being disclosed. Our objectives in this systematic review were to: (a) characterize known re-identification attacks on health data and contrast that to re-identification attacks on other kinds of data, (b) compute the overall proportion of records that have been correctly re-identified in these attacks, and (c) assess whether these demonstrate weaknesses in current de-identification methods. Searches were conducted in IEEE Xplore, ACM Digital Library, and PubMed. After screening, fourteen eligible articles representing distinct attacks were identified. On average, approximately a quarter of the records were re-identified across all studies (0.26 with 95% CI 0.046-0.478) and 0.34 for attacks on health data (95% CI 0-0.744). There was considerable uncertainty around the proportions as evidenced by the wide confidence intervals, and the mean proportion of records re-identified was sensitive to unpublished studies. Two of fourteen attacks were performed with data that was de-identified using existing standards. Only one of these attacks was on health data, which resulted in a success rate of 0.00013. The current evidence shows a high re-identification rate but is dominated by small-scale studies on data that was not de-identified according to existing standards. This evidence is insufficient to draw conclusions about the efficacy of de-identification methods.