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Insensitive name to people actually suffering from amnesia, (me).
I am sorry, but I disagree.
Does a white person truly know about how offensive it is to call an African American the n-word? No. He/She won't without becoming African American himself. It's the same story here.
Fair warning: anonymization is a hard problem. It is never easy, and you'd be surprised how many bits can leak out of what you thought was properly anonymized data. If you are using data for test purposes please use generated data, not anonymized data. This has the additional advantage that there is no potential path for live data to end up on a developers machine. added in edit: And also realize that just using a se…
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https://en.wikipedia.org/wiki/K-anonymity#Methods_for_k-anon...
k-anonymity is often only applied to "pseudoidentifiers", if you have the original dataset it'd be trivial to reverse k-anonymity applied that way. For example someone's blood pressure isn't considered an identifying variable, and would not need to be anonymised (should not too, to keep data utility high), however this would make linking against the original dataset trivial.
Insensitive name to people actually suffering from amnesia, (me).
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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 protecting high-dimensional data.
Are there good ways to measure the amount (original) subject level data that can be extracted from a synthetic dataset, or calculated risk of reidentification (which is nice and easy for k-anonymity (if your assumptions are valid))?
If Im looking for a white male in new york city it's going to be harder to find my target than it would be if I also know their birth date and zip code.
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> Data anonymized with Amnesia are statistically guaranteed that they cannot be linked to the original data. It looks like (from other text on their site) they use variants on k-anonymity. This can prevent re-linking attacks back to the original data, but we've also known for a decade that this isn't especially strong. For example, two independent k-anonymous releases can unique identify everyone in the dataset[0]. […
However that statistical guarantee also requires your pseudoidentifiers to be picked correctly, i.e. it only holds true if you select all variables the attacker could possibly know about a subject. I think that is the hard part here, it's not something I would recommend someone doing without a lot of research and experience for highly dimensional data.
I feel like differential privacy is the strongest definition we have, but it is also lacking from a practical standpoint. What does it mean to have N nats/bits of information gain from seeing the result of a query? How does this translate to my risk of a PII leak?
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If someone makes inferences on the de-identified data, or joins it against another dataset. The source dataset lets those inferences or joins be tied back to the original identifying data. The main point is that de-identified data can still be "personal" so it's regulated. If you share or make public psuedonymous data, that data is still covered by GDPR so you have to inform the individuals, have a legal basis (such…
> The source dataset lets those inferences or joins be tied back to the original identifying data. But if the attacker lacks the source dataset, they can't do this, and if they possess the source dataset, they'd use it for their analysis rather than using the anonymised dataset.
Oh, there is Netflix account for user # 188da24a7789d and the IRS released tax summaries for user # 188da24a7789d? That's interesting, since I know that user # 188da24a7789d is really MaxBarraclough.