3 papers
cs.IR2025
Preserving Privacy and Utility in LLM-Based Product Recommendations
Tina Khezresmaeilzadeh, Jiang Zhang, Dimitrios Andreadis +1
Large Language Model (LLM)-based recommendation systems leverage powerful language models to generate personalized suggestions by processing user interactions and preferences. Unli…
cs.LG2025
SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models
Jiang Zhang, Rohan Xavier Sequeira, Konstantinos Psounis
Specialized machine learning (ML) models tailored to users needs and requests are increasingly being deployed on smart devices with cameras, to provide personalized intelligent ser…
cs.CR2024
Differentially Private Federated Learning without Noise Addition: When is it Possible?
Jiang Zhang, Konstantinos Psounis, Salman Avestimehr
Federated Learning (FL) with Secure Aggregation (SA) has gained significant attention as a privacy preserving framework for training machine learning models while preventing the se…