28 citations · 66 across the 23 of their papers we have counts for
13 papers · 1 filter
DP-VOXLET: Provable Speaker Anonymization for Disentangled Speech Representations
Ivoline Ngong, Jack D'Iorio, Hailey Schoppe +4
Systems for speaker anonymization obfuscate the speaker of an utterance, while maintaining its original semantic contents and prosody. Recent solutions for speaker anonymization re…
Distributed HDMM: Scalable, Distributed, Accurate, and Differentially Private Query Workloads without a Trusted Curator
Ratang Sedimo, Ivoline C. Ngong, Jami Lashua +1
We present the Distributed High-Dimensional Matrix Mechanism (Distributed HDMM), a protocol for answering workloads of linear queries on distributed data that provides the accuracy…
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…
Language-Based Security 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. Currently, proof methods for low-level MPC protocols a…
OLYMPIA: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocols
Ivoline C. Ngong, Nicholas Gibson, Joseph P. Near
Recent secure aggregation protocols enable privacy-preserving federated learning for high-dimensional models among thousands or even millions of participants. Due to the scale of t…
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…
Secret Sharing Sharing For Highly Scalable Secure Aggregation
Timothy Stevens, Joseph Near, Christian Skalka
Secure Multiparty Computation (MPC) can improve the security and privacy of data owners while allowing analysts to perform high quality analytics. Secure aggregation is a secure di…
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors
Timothy Stevens, Christian Skalka, Christelle Vincent +3
Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differen…
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…
PrivFramework: A System for Configurable and Automated Privacy Policy Compliance
Usmann Khan, Lun Wang, Jithendaraa Subramanian +2
Today's massive scale of data collection coupled with recent surges of consumer data leaks has led to increased attention towards data privacy and related risks. Conventional data…
DuetSGX: Differential Privacy with Secure Hardware
Phillip Nguyen, Alex Silence, David Darais +1
Differential privacy offers a formal privacy guarantee for individuals, but many deployments of differentially private systems require a trusted third party (the data curator). We…
Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms
Noah Johnson, Joseph P. Near, Joseph M. Hellerstein +1
Differential privacy is fast becoming the gold standard in enabling statistical analysis of data while protecting the privacy of individuals. However, practical use of differential…
Towards Practical Differential Privacy for SQL Queries
Noah Johnson, Joseph P. Near, Dawn Song
Differential privacy promises to enable general data analytics while protecting individual privacy, but existing differential privacy mechanisms do not support the wide variety of…