WHY IT MATTERS: Digital Transformation

Make no mistake, the knowledge of hacking is different from a hacker. A hacker is a person, not the skill. And the use of any knowledge is a personal choice and that choice isn’t universal. The same way some politicians are corrupt, some hackers are corrupt. The same way some politicians are good, some hackers are good also. You may be a Satoshi Nakamoto or an Albert Gonzalez, it is your choice. There is nothing wrong with the hacker knowledge. It is all about you. What You’ll Learn

While rich medical, behavioral, and socio-demographic data are key to modern data-driven research, their collection and use raise legitimate privacy concerns. Anonymizing datasets through de-identification and sampling before sharing them has been the main tool used to address those concerns. We here propose a generative copula-based method that can accurately estimate the likelihood of a specific person to be correctly re-identified, even in a heavily incomplete dataset. On 210 populations, our method obtains AUC scores for predicting individual uniqueness ranging from 0.84 to 0.97, with low false-discovery rate. Using our model, we find that 99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes. Our results suggest that even heavily sampled anonymized datasets are unlikely to satisfy the modern standards for anonymization set forth by GDPR and seriously challenge the technical and legal adequacy of the de-identification release-and-forget model. Anonymization has been the main means of addressing privacy concerns in sharing medical and socio-demographic data. Here, the authors estimate the likelihood that a specific person can be re-identified in heavily incomplete datasets, casting doubt on the adequacy of current anonymization practices.

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