3 citations · 11 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models
Jiang Zhang, Qiong Wu, Yiming Xu +3
Toxic content detection is crucial for online services to remove inappropriate content that violates community standards. To automate the detection process, prior works have propos…
A Utility-Preserving Obfuscation Approach for YouTube Recommendations
Jiang Zhang, Hadi Askari, Konstantinos Psounis +1
Online content platforms optimize engagement by providing personalized recommendations to their users. These recommendation systems track and profile users to predict relevant cont…
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?
Ahmed Roushdy Elkordy, Jiang Zhang, Yahya H. Ezzeldin +2
Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device.…
Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation
Jiang Zhang, Lillian Clark, Matthew Clark +2
Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network p…
Location Leakage in Federated Signal Maps
Evita Bakopoulou, Mengwei Yang, Jiang Zhang +2
We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices. We formulate the problem within the online f…
HARPO: Learning to Subvert Online Behavioral Advertising
Jiang Zhang, Konstantinos Psounis, Muhammad Haroon +1
Online behavioral advertising, and the associated tracking paraphernalia, poses a real privacy threat. Unfortunately, existing privacy-enhancing tools are not always effective agai…