197 citations · 299 across the 47 of their papers we have counts for
8 papers · 1 filter
Prediction with Expert Advice under Local Differential Privacy
Ben Jacobsen, Kassem Fawaz
We study the classic problem of prediction with expert advice under the constraint of local differential privacy (LDP). In this context, we first show that a classical algorithm na…
Auto-SPT: Automating Semantic Preserving Transformations for Code
Ashish Hooda, Mihai Christodorescu, Chuangang Ren +3
Machine learning (ML) models for code clone detection determine whether two pieces of code are semantically equivalent, which in turn is a key building block for software-engineeri…
Personalizing Agent Privacy Decisions via Logical Entailment
James Flemings, Ren Yi, Octavian Suciu +3
Personal large language model (LLM) agents increasingly perform tasks that require access to user data, raising concerns about appropriate data disclosure. We show that relying sol…
Benchmarking LLM Privacy Recognition for Social Robot Decision Making
Dakota Sullivan, Shirley Zhang, Jennica Li +3
While robots have previously utilized rule-based systems or probabilistic models for user interaction, the rapid evolution of large language models (LLMs) presents new opportunitie…
What Really is a Member? Discrediting Membership Inference via Poisoning
Neal Mangaokar, Ashish Hooda, Zhuohang Li +5
Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often…
Private Continual Counting of Unbounded Streams
Ben Jacobsen, Kassem Fawaz
We study the problem of differentially private continual counting in the unbounded setting where the input size is not known in advance. Current state-of-the-art algorithms bas…
Exploring Families' Use and Mediation of Generative AI: A Multi-User Perspective
Shirley Zhang, Dakota Sullivan, Jennica Li +4
Generative AI (GenAI) platforms, such as ChatGPT, have gained popularity among the public due to their ease of access, use, and support of educational and creative activities. Desp…
"Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications
Jack West, Bengisu Cagiltay, Shirley Zhang +3
Machine learning models deployed locally on social media applications are used for features, such as face filters which read faces in-real time, and they expose sensitive attribute…