3 citations · 7 across the 6 of their papers we have counts for
13 papers
SMT-Boosted Security Types for Low-Level MPC
Christian Skalka, Joseph P. Near
Secure Multi-Party Computation (MPC) is an important enabling technology for data privacy in modern distributed applications. We develop a new type theory to automatically enforce…
Improving Utility for Privacy-Preserving Analysis of Correlated Columns using Pufferfish Privacy
Krystal Maughan, Joseph P. Near
Surveys are an important tool for many areas of social science research, but privacy concerns can complicate the collection and analysis of survey data. Differentially private anal…
Backpropagation Clipping for Deep Learning with Differential Privacy
Timothy Stevens, Ivoline C. Ngong, David Darais +3
We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning. Our approach clips each tr…
Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers
Krystal Maughan, Ivoline C. Ngong, Joseph P. Near
As AI-based systems increasingly impact many areas of our lives, auditing these systems for fairness is an increasingly high-stakes problem. Traditional group fairness metrics can…
Do I Get the Privacy I Need? Benchmarking Utility in Differential Privacy Libraries
Gonzalo Munilla Garrido, Joseph Near, Aitsam Muhammad +3
An increasing number of open-source libraries promise to bring differential privacy to practice, even for non-experts. This paper studies five libraries that offer differentially p…
Solo: A Lightweight Static Analysis for Differential Privacy
Chike Abuah, David Darais, Joseph P. Near
All current approaches for statically enforcing differential privacy in higher order languages make use of either linear or relational refinement types. A barrier to adoption for t…