25 citations · 69 across the 7 of their papers we have counts for
7 papers
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy
Xiaolan Gu, Ming Li, Li Xiong
Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Ex…
Wireless Federated Learning with Local Differential Privacy
Mohamed Seif, Ravi Tandon, Ming Li
In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (…
GhostImage: Remote Perception Attacks against Camera-based Image Classification Systems
Yanmao Man, Ming Li, Ryan Gerdes
In vision-based object classification systems imaging sensors perceive the environment and machine learning is then used to detect and classify objects for decision-making purposes…
Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation
Bo Jiang, Ming Li, Ravi Tandon
In this paper, we study local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users' privacy without relying on a trusted th…
PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility
Xiaolan Gu, Ming Li, Yueqiang Cheng +2
Data collection under local differential privacy (LDP) has been mostly studied for homogeneous data. Real-world applications often involve a mixture of different data types such as…
Providing Input-Discriminative Protection for Local Differential Privacy
Xiaolan Gu, Ming Li, Li Xiong +1
Local Differential Privacy (LDP) provides provable privacy protection for data collection without the assumption of the trusted data server. In the real-world scenario, different d…