51–60 of 101 posts
edit: shameless plug. check out tonic.ai for a solution to the above problem.
where's the link to code?
The paper suggests it can be accessed on the site [0], however certain parts of the site only appear if you run through their questionnaires. > The source code to reproduce the experiments is available at https://cpg.doc.ic.ac.uk/individual-risk , along with documentation, tests, and examples. As far as I can tell, the source code is not available, at least not from where the authors suggest. [0] https://cpg.doc.ic.a…
The article title misses a bit of nuance from the paper which is specifically talking about re-identification. e.g from the paper: "We show that, as a male born on July 31, 1945 and living in Cambridge (02138), the information used by Latanya Sweeney at the time, William Weld was unique with a 58% likelihood (ξx = 0.58 and κx = 0.77), meaning that Latanya Sweeney’s re-identification had 77% chances of being correct.…
What accounts for that remaining 2‰ of uncertainty?
The article title misses a bit of nuance from the paper which is specifically talking about re-identification. e.g from the paper: "We show that, as a male born on July 31, 1945 and living in Cambridge (02138), the information used by Latanya Sweeney at the time, William Weld was unique with a 58% likelihood (ξx = 0.58 and κx = 0.77), meaning that Latanya Sweeney’s re-identification had 77% chances of being correct.…
What accounts for that remaining 2‰ of uncertainty?
If they can pick out individuals from the data, then the data is not anonymized. Sure they may have unassociated data spread across unassociated records, but if an algorithm can pick it out, then so could a human (though way more effort). That for me is not anonymized data.
> That for me is not anonymized data. What matters is that this is what most online companies (and their terms of service) would call anonymized data.
I'm a programmer in the GP data analysis world. We use the term 'pseudonymization' for this kind of data. 'Anonymization' is used solely to refer to, say, 'the sum total of diabetes patients this practice has' (that would be anonymous patient data; it would not be anonymous relative to the GP office this refers to): Aggregated data that can no longer be reduced to a single individual at all. The term raises questions…
Earlier quoted context omitted.
Yes, in the sense that the output of a differentially private protocol has mathematical guarantees against re-identification, regardless of the computational power or side information an adversary has. There are caveats. The exact strength of the privacy guarantee depends on the parameters you use and the number of computations you do, so simply saying "we use a differentially private algorithm" doesn't guarantee pri…
do you have some examples?
I recommend watching it if you're interested at https://homepages.cwi.nl/~boncz/sigmod-pods2019.html (top-left vid)
(as a side-note Frank McSherry received SIGMOD Test Of Time Award for his Differential Privacy paper at the same conference).
Earlier quoted context omitted.
> That for me is not anonymized data. What matters is that this is what most online companies (and their terms of service) would call anonymized data.
If they operate in Europe then I am pretty sure that the GDPR legislation is pretty straight forward here. If you can de-anonymize the data then it is by definition not anonymized.
Of the entities that remain, they fall into two buckets: Ones powerful enough that they already have personally identifiable data without the need to deanonymize anonymous data sets and ones small enough that they don't have the capabilities to deanonymize.
If you're a government, you don't need to rely on anonymized data sets, you have the sets with the labels already. If you're a stalker or internet troll or whatever, it's far easier to just pay one of the PI websites $29 to get far more data on a person than any deanonymized dataset will give you.