Luc Rocher, Julien M. Hendrickx, Yves-Alexandre de Montjoye
2019 · Nature Communications
Abstract 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.
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Still, it is _personal_ data collected and sold by Google, which was the point raised by gp comment. As for it being personally identifying, the aggregation/pseudonymization/anonymization process doesn't even prevent precise identification [0]. I'd say it's pretty close. [0]: https://www.nature.com/articles/s41467-019-10933-3
I'm going to repeat myself as I do everytime I encounter such tools. These tools DO NOT provide anonymization, and especially not at the level required by the EU's GDPR (where the notion of PII does not exist). As a computer scientist and academic researcher having worked on this topic for now more than a decade (some of my work if you are interested: [1, 2]), re-identification is often possible from few pieces of information. Masking or replacing a few values or columns will often not provide sufficient guarantees—especially when a lot of information is being released. What this tool does is called ‘pseudonymization’ and maybe, if not very carefully, ‘de-identification’…
in some case. With colleagues, reviewed all the literature and industry practices a few months ago [3], and our conclusion was: > We find that, although no perfect solution exists, applying modern techniques while auditing their guarantees against attacks is the best approach to safely use and share data today. This is clearly not what this tool is doing. [1] https://www.nature.com/articles/s41467-019-10933-3 [2] https://www.nature.com/articles/s41467-024-55296-6 [3] https://www.science.org/doi/10.1126/sciadv.adn7053
Incredibly naive. Easy to de-anonymize. First they come for the books, now AI practitioners come for the health data. "Estimating the success of re-identifications in incomplete datasets using generative models" - https://www.nature.com/articles/s41467-019-10933-3 "...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 ..."
Your IP address and a timestamp are enough to give google a pretty good idea who you are in many cases. If you log into any one of their services or use one of their products using that same IP address it's a pretty strong indicator. It takes surprisingly little "non-identifiable information" to figure out someone's identify. (for example: https://www.nature.com/articles/s41467-019-10933-3) I'm pretty sure google has enough data to put 1 and 1 together the vast majority of the time.
Considering all the metadata they can collect over indefinite periods of time, with 100% certainty, unless you invest extraordinary effort to give them enough misleading data. There are plenty of studies (example: https://www.nature.com/articles/s41467-019-10933-3/) that show you need only limited amounts of data to be able to deanonymize people.
Also works for health data as well[0] if anonymity is actually important it's really hard to collect data at all. [0] https://www.nature.com/articles/s41467-019-10933-3
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