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).
Synthea is a Synthetic Patient Population Simulator. The goal is to output synthetic, realistic (but not real), patient data and associated health records in a variety of formats.
Nice offline tool to generate synthetic patient data..
Fascinating paper: “The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets”, Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, Dawn Song at https://arxiv.org/abs/1802.08232
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.”
This is one of my favorite documents that I refer to on a day to day basis.
Nice list of privacy risks and severity examples.
The Norwegian DPA has given Gator AS orders to discontinue all processing of personal information about its customers since they have not provided enough information in the smart bells they provide. In addition, PepCall AS and GPS for children – Smartprodukt AS have been notified of similar decisions.
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The Australian government published a de-identified open health data set in the past, which contained the patient data of a subset of the Australian population. – The de-identification process involved not just stripping direct identifiers, but also adding some inaccuracies to the data set. However, the data set was still at the person-level.
Researchers have been able to successfully re-identify some patients.
Continue reading “Researchers re-identify patients from a de-identified patient data set published by the Australian government”