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A Unified Framework for Adversary-Aware Differential Privacy Bounds
Marika Swanberg, Meenatchi Sundaram Muthu Selva Annamalai, Jamie Hayes +2
Differential Privacy (DP) bounds the privacy leakage of a mechanism against worst-case membership inference, but the precise tradeoff between complex adversarial models and DP prot…
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
Rachel Cummings, Shlomi Hod, Jayshree Sarathy +1
Differential Privacy (DP) is a mathematical framework that is increasingly deployed to mitigate privacy risks associated with machine learning and statistical analyses. Despite the…
DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection
Marika Swanberg, Damien Desfontaines, Samuel Haney
Partition selection, or set union, is an important primitive in differentially private mechanism design: in a database where each user contributes a list of items, the goal is to p…
Control, Confidentiality, and the Right to be Forgotten
Aloni Cohen, Adam Smith, Marika Swanberg +1
Recent digital rights frameworks give users the right to delete their data from systems that store and process their personal information (e.g., the "right to be forgotten" in the…
Improved Differentially Private Analysis of Variance
Marika Swanberg, Ira Globus-Harris, Iris Griffith +3
Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is…