[HN Gopher] Understanding privacy risk with k-anonymity and l-di...
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       Understanding privacy risk with k-anonymity and l-diversity
        
       Author : marols
       Score  : 65 points
       Date   : 2024-11-05 09:30 UTC (3 days ago)
        
 (HTM) web link (marcusolsson.dev)
 (TXT) w3m dump (marcusolsson.dev)
        
       | p4bl0 wrote:
       | People interested in this will probably also like reading this
       | friendly introduction to differential privacy:
       | https://desfontain.es/blog/friendly-intro-to-differential-pr...,
       | which is friendly yet goes into a lot of details and techniques
       | in a long series of blog posts.
        
         | porridgeraisin wrote:
         | Yes, that is a great resource. And if you're looking for a
         | connection to ML, the search term is "DP-SGD".
        
         | 3np wrote:
         | Also great as well as entertaining: https://gwern.net/death-
         | note-anonymity
        
       | isaacremuant wrote:
       | > Imagine you're a data analyst at a global company who's been
       | asked to provide employee statistics for a survey on remote
       | working and distributed teams
       | 
       | I'm going to go on a limb and assume the exercise of the analysis
       | is just for fun and that the decision was already made by the
       | strategic team of people who never go to the office themselves or
       | aren't really affected by it.
       | 
       | The 0 to 3 day move that many did was not supported by data in
       | any of the cases I've seen and had more to do with government
       | pushes and saving downtown or encouraging self layoffs.
       | 
       | The article itself on the technique is fine although I'd also
       | make sure to include the percentage with regards to non
       | participants because trust in anonymity over a contentious topic
       | will definitely affect who answers and what they answer.
        
       | FjordWarden wrote:
       | No mention of ARX, but it is also a tool that lets you calculate
       | those metrics: https://arx.deidentifier.org
        
       | mentalgear wrote:
       | This reminds me of when Apple first introduced, with great
       | fanfare, their pivot to privacy-first and "Differential Privacy."
       | 
       | However, when privacy experts later examined Apple's
       | implementation, they found that the promised privacy was largely
       | an illusion. The parameters Apple had chosen for their
       | Differential Privacy were so weak that only a few data exchanges
       | would be enough to de-anonymize individual users.
       | 
       | I don't know if they improved on it, but back then it was less
       | about true privacy and more about the appearance of privacy and
       | an unfortunate example of marketing (core differentiator, premium
       | justification) taking precedence over meaningful protection.
        
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       (page generated 2024-11-08 23:01 UTC)