K-anonymity
en.wikipedia.org
K-anonymity
1–10 of 12 posts
Re: K-anonymity
#2https://www.troyhunt.com/ive-just-launched-pwned-passwords-v...
https://blog.cloudflare.com/validating-leaked-passwords-with...
Re: K-anonymity
#3Re: K-anonymity
#4Follow-ups:
- k-map: https://desfontain.es/privacy/k-map.html
- l-diversity: https://desfontain.es/privacy/l-diversity.html
- δ-presence: https://desfontain.es/privacy/delta-presence.html
- differential privacy: https://desfontain.es/privacy/differential-privacy-awesomene...
Re: K-anonymity
#5If you’re curious about the matter, there’s an alternative technique called differential privacy that adds randomization to the data, and is able to provide guarantees that individual records cannot be identified. k-anonymity is subject to e.g. linkage attacks of the kind that differential privacy seeks to eliminate.
Re: K-anonymity
#6A recent common usage is by the https://haveibeenpwned.com leaked password check database, worked on in part by the Cloudflare team: https://www.troyhunt.com/ive-just-launched-pwned-passwords-v... https://blog.cloudflare.com/validating-leaked-passwords-with...
https://www.okta.com/blog/2018/05/add-passprotect-to-your-we...
Re: K-anonymity
#7If you’re curious about the matter, there’s an alternative technique called differential privacy that adds randomization to the data, and is able to provide guarantees that individual records cannot be identified. k-anonymity is subject to e.g. linkage attacks of the kind that differential privacy seeks to eliminate.
Re: K-anonymity
#8A recent common usage is by the https://haveibeenpwned.com leaked password check database, worked on in part by the Cloudflare team: https://www.troyhunt.com/ive-just-launched-pwned-passwords-v... https://blog.cloudflare.com/validating-leaked-passwords-with...
Okta's PassProtect Chrome extension also uses k-anonymity. https://www.okta.com/blog/2018/05/add-passprotect-to-your-we...
Re: K-anonymity
#9https://github.com/KIProtect/data-privacy-for-data-scientist...
In the workshop we implement the "Mondrian algorithm" to produce a k-anonymous dataset. We then look at the problems of this approach (i.e. missing diversity in the sensitive attribute) and try to fix it using l-diversity (which is also not optimal) and finally t-closeness. The third notebook includes an implementation of a differentially private "randomized response" scheme, showing how it changes the data and how we can take into account the added noise when working with the randomized data.
I think it's important to keep in mind that k-anonymity and differential privacy are not algorithms but mathematical privacy definitions. To implement them, you need a suitable method like the "Mondrian" algorithm or a randomized response scheme.
If you have any questions or suggestions for improvements please open an issue or PR on Github!