m0nster

4 ms·

1.
by m0nster 6y ago
Absolutely. They're doing a great job at UKAN!
2.
by m0nster 6y ago
True. For this reason, even anonymous data can usually not be shared as open data. You have to control the environment in which the data is used to control what is "reasonably likely" (see also comment by La1n above).
3.
by m0nster 6y ago
Also with synthetic data, there is an inherent trade-off between privacy risks and the usefulness of the data produced. However, this trade-off can be of a different nature, resulting in advantages for synthetization, for example when prote
4.
by m0nster 6y ago
In my experience, this is a question of interpretation (see e.g. Recital 26 and the question of what is "reasonably likely"). You can ask ten different experts, and you will get ten different opinions. Unfortunately, many aspects
5.
by m0nster 6y ago
True, Amnesia can also be run locally!
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by m0nster 6y ago
ARX (see other comment in this thread) also supports data anonymization for privacy-preserving machine learning.
7.
by m0nster 6y ago
If you're interested in tools such as Amnesia, you might also want to take a look at ARX, which supports much more anonymization methods, including Differential Privacy: https://arx.deidentifier.org https://githu
8.
by m0nster 8y ago
ARX [1, 2] is an open source software that (among other features) supports most of the methods mentioned in this thread. Full disclosure: I'm one of the developers of ARX. [1] Website: http://arx.deidentifier.org [2] Source
9.
by m0nster 11y ago
While data de-identification surely has its limits, it is useful in many contexts. If someone is interested in tools for data de-identification, ARX [1, 2] is an open source software that (among other features) supports exactly the set of m