May 05, 2025
The Curse of Dimensionality: De-identification Challenges in the Sharing of Highly Dimensional Datasets
[…] the input dataset. This limits what an adversary can infer about any individual from the output. Privacy loss is quantified by parameters \epsilon (epsilon) and sometimes \ delta (delta), where lower values mean stronger privacy. DP guarantees are robust against arbitrary background knowledge and compose predictably (the total privacy loss from multiple DP analyses […]