To add to that, if there are concerns about data being de-anonymized, there are statistical techniques to mitigate it.
Do you or anybody else reading this have experience with differential privacy techniques on relatively small datasets (less than 10k people, say)? I’ve only heard of differential privacy used in the context of machine learning and massive datasets.
Well, I am far from expert, but my understanding is that differential privacy operates on queries as opposed to individual datapoints. But there are tools s.a. randomized response which will provide plausible deniability to individual responses.
To add to that, if there are concerns about data being de-anonymized, there are statistical techniques to mitigate it.
Do you or anybody else reading this have experience with differential privacy techniques on relatively small datasets (less than 10k people, say)?
I’ve only heard of differential privacy used in the context of machine learning and massive datasets.
Well, I am far from expert, but my understanding is that differential privacy operates on queries as opposed to individual datapoints. But there are tools s.a. randomized response which will provide plausible deniability to individual responses.