I keep going back to this resource, as it has a good set of examples for privacy risks.

But it also has a long catalog of technical and organizational measures (TOM).

Nice offline tool to generate synthetic patient data..

https://github.com/synthetichealth/synthea

https://www.cnil.fr/en/how-can-humans-keep-upper-hand-report-ethical-matters-raised-algorithms-and-artificial-intelligence

Norway

https://www.datatilsynet.no/globalassets/global/english/ai-and-privacy.pdf

UK

https://ico.org.uk/for-organisations/guide-to-data-protection/big-data/

Turns out that your algorithm memorizes your secrets in the training data. -Even if the algorithm is a lot smaller than the actual secrets… – My jaw fell do the ground right here :

“The fact that models completely memorize secrets in the training data is completely unexpected: our language model is only 600KB when compressed , and the PTB dataset is 1.7MB when compressed. Assuming that the PTB dataset can not be compressed significantly more than this, it is therefore information-theoretically impossible for the model to have memorized all training data—it simply does not have enough capacity with only 600KB of weights. Despite this, when we repeat our experiment and train this language model multiple times, the inserted secret is the most likely 80% of the time (and in the remaining times the secret is always within the top10 most likely). At present we are unable to fully explain the reason this occurs. We  conjecture that the model learns a lossy compression of the training data on which it is forced to learn and generalize. But since secrets are random, incompressible parts of the training data, no such force prevents the model from simply memorizing their exact details.”

https://arxiv.org/pdf/1802.08232.pdf

Nice list of privacy risks and severity examples.

https://www.cnil.fr/sites/default/files/typo/document/CNIL-PIA-2-Tools.pdf

https://www.hldataprotection.com/2018/01/articles/health-privacy-hipaa/aetna-17-2-million-breach-settlement-brings-lessons-for-handling-health-data/

Use right-click in Chrome to translate:

https://www.datatilsynet.no/aktuelt/2017/palegger-stans-i-behandlingen-av-personopplysninger-i-smartklokker/

Researchers have been able to successfully re-identify some patients.

Abstract: With the aim of informing sound policy about data sharing and privacy, we describe successful re-identification of patients in an Australian de-identified open health dataset. As in prior studies of similar datasets, a few mundane facts often suffice to isolate an individual.
Some people can be identified by name based on publicly available information. Decreasing the precision of the unit-record level data, or perturbing it statistically, makes re-identification gradually harder at a substantial cost to utility. We also examine the value of related datasets in improving the accuracy and confidence of re-identification. Our re-identifications were performed on a 10% sample dataset, but a related open Australian dataset allows us to infer with high confidence that some individuals in the sample have been correctly re-identified.
Finally, we examine the combination of the open datasets with some commercial datasets that are known to exist but are not in our possession. We show that they would further increase the ease of re-identification

https://arxiv.org/ftp/arxiv/papers/1712/1712.05627.pdf