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cs.CR2026
Keeping a Secret Requires a Good Memory: Space Lower-Bounds for Private Algorithms
Alessandro Epasto, Xin Lyu, Pasin Manurangsi
We study the computational cost of differential privacy in terms of memory efficiency. While the trade-off between accuracy and differential privacy is well-understood, the inheren…
cs.CR2025
Differentially Private Synthetic Data Release for Topics API Outputs
Travis Dick, Alessandro Epasto, Adel Javanmard +5
The analysis of the privacy properties of Privacy-Preserving Ads APIs is an area of research that has received strong interest from academics, industry, and regulators. Despite thi…
cs.CR2023
Measuring Re-identification Risk
CJ Carey, Travis Dick, Alessandro Epasto +8
Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification r…