2 citations · 5 across the 8 of their papers we have counts for
8 papers
PE-means: Improved Differentially Private -means Clustering through Private Evolution
Thomas Humphries, Zinan Lin, Sergey Yekhanin
We study the problem of differentially private (DP) -means clustering in Euclidean space. Previous solutions rely on summing the private data directly, which induces a sensitivi…
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…
Selective MPC: Distributed Computation of Differentially Private Key-Value Statistics
Thomas Humphries, Rasoul Akhavan Mahdavi, Shannon Veitch +1
Key-value data is a naturally occurring data type that has not been thoroughly investigated in the local trust model. Existing local differentially private (LDP) solutions for comp…
Investigating Membership Inference Attacks under Data Dependencies
Thomas Humphries, Simon Oya, Lindsey Tulloch +4
Training machine learning models on privacy-sensitive data has become a popular practice, driving innovation in ever-expanding fields. This has opened the door to new attacks that…