28 citations · 66 across the 23 of their papers we have counts for
25 papers
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…
Qoreo: Choreographic Programming for Quantum Distributed Systems
Jennifer Paykin, Steven Baldasty, Joseph P. Near +1
Programming distributed quantum systems requires multiple actors to coordinate precise sequences of quantum operations, classical communication, and entanglement generation. Writin…
Differentially Private Multimodal In-Context Learning
Ivoline C. Ngong, Zarreen Reza, Joseph P. Near
Vision-language models are increasingly applied to sensitive domains such as medical imaging and personal photographs, yet existing differentially private methods for in-context le…
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…
Differentially Private Learning Needs Better Model Initialization and Self-Distillation
Ivoline C. Ngong, Joseph P. Near, Niloofar Mireshghallah
Differentially private SGD (DPSGD) enables privacy-preserving training of language models, but often reduces utility, diversity, and linguistic quality. We introduce DPRefine, a th…
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…
Evaluating the Usability of Differential Privacy Tools with Data Practitioners
Ivoline C. Ngong, Brad Stenger, Joseph P. Near +1
Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in real-world datasets and systems remains challenging. Recently de…
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…
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…
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…
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…
DDUO: General-Purpose Dynamic Analysis for Differential Privacy
Chike Abuah, Alex Silence, David Darais +1
Differential privacy enables general statistical analysis of data with formal guarantees of privacy protection at the individual level. Tools that assist data analysts with utilizi…
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…
Towards Auditability for Fairness in Deep Learning
Ivoline C. Ngong, Krystal Maughan, Joseph P. Near
Group fairness metrics can detect when a deep learning model behaves differently for advantaged and disadvantaged groups, but even models that score well on these metrics can make…
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…
Towards a Measure of Individual Fairness for Deep Learning
Krystal Maughan, Joseph P. Near
Deep learning has produced big advances in artificial intelligence, but trained neural networks often reflect and amplify bias in their training data, and thus produce unfair predi…
Duet: An Expressive Higher-order Language and Linear Type System for Statically Enforcing Differential Privacy
Joseph P. Near, David Darais, Chike Abuah +8
During the past decade, differential privacy has become the gold standard for protecting the privacy of individuals. However, verifying that a particular program provides different…
Data Capsule: A New Paradigm for Automatic Compliance with Data Privacy Regulations
Lun Wang, Joseph P. Near, Neel Somani +4
The increasing pace of data collection has led to increasing awareness of privacy risks, resulting in new data privacy regulations like General data Protection Regulation (GDPR). S…
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…