TKDE 18《A Utility-optimized Framework for Personalized Private Histogram Estimation》
Now, we elaborate how to derive an $\epsilon$-private version from the existing data.
Local differential privacy is a de facto concept to defend user privacy without any reliance on trusted third-parties
- The widespread acceptance of differential privacy has led to the
publication of many sophisticated algorithms for protecting privacy.
