2 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Interpreting the Error of Differentially Private Median Queries through Randomization Intervals
Thomas Humphries, Tim Li, Shufan Zhang +2
It can be difficult for practitioners to interpret the quality of differentially private (DP) statistics due to the added noise. One method to help analysts understand the amount o…
FastLloyd: Federated, Accurate, Secure, and Tunable -Means Clustering with Differential Privacy
Abdulrahman Diaa, Thomas Humphries, Florian Kerschbaum
We study the problem of privacy-preserving -means clustering in the horizontally federated setting. Existing federated approaches using secure computation suffer from substantia…
PEPSI: Practically Efficient Private Set Intersection in the Unbalanced Setting
Rasoul Akhavan Mahdavi, Nils Lukas, Faezeh Ebrahimianghazani +7
Two parties with private data sets can find shared elements using a Private Set Intersection (PSI) protocol without revealing any information beyond the intersection. Circuit PSI p…
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Abdulrahman Diaa, Lucas Fenaux, Thomas Humphries +9
Machine Learning as a Service (MLaaS) is an increasingly popular design where a company with abundant computing resources trains a deep neural network and offers query access for t…
Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration
Miti Mazmudar, Thomas Humphries, Jiaxiang Liu +2
Differential privacy (DP) allows data analysts to query databases that contain users' sensitive information while providing a quantifiable privacy guarantee to users. Recent intera…