3 papers
cs.CR2025
Approximate Differential Privacy of the Mechanism
Matthew Joseph, Alex Kulesza, Alexander Yu
We study the mechanism for computing a -dimensional statistic with bounded sensitivity under approximate differential privacy. Across a range of privacy parame…
cs.CR2024
Practical Considerations for Differential Privacy
Kareem Amin, Alex Kulesza, Sergei Vassilvitskii
Differential privacy is the gold standard for statistical data release. Used by governments, companies, and academics, its mathematically rigorous guarantees and worst-case assumpt…
cs.DS2023
Mean estimation in the add-remove model of differential privacy
Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
Differential privacy is often studied under two different models of neighboring datasets: the add-remove model and the swap model. While the swap model is frequently used in the ac…