225 citations · 805 across the 24 of their papers we have counts for
8 papers · 1 filter
Robust Deep Learning Ensemble against Deception
Wenqi Wei, Ling Liu
Deep neural network (DNN) models are known to be vulnerable to maliciously crafted adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the train…
Utility-Optimized Synthesis of Differentially Private Location Traces
Mehmet Emre Gursoy, Vivekanand Rajasekar, Ling Liu
Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their locatio…
Data Poisoning Attacks Against Federated Learning Systems
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy +1
Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep neural networks in which participants' data remains on their own devices with only mode…
Understanding Object Detection Through An Adversarial Lens
Ka-Ho Chow, Ling Liu, Mehmet Emre Gursoy +3
Deep neural networks based object detection models have revolutionized computer vision and fueled the development of a wide range of visual recognition applications. However, recen…
Bitcoin Transaction Forecasting with Deep Network Representation Learning
Wenqi Wei, Qi Zhang, Ling Liu
Bitcoin and its decentralized computing paradigm for digital currency trading are one of the most disruptive technology in the 21st century. This paper presents a novel approach to…
LDP-Fed: Federated Learning with Local Differential Privacy
Stacey Truex, Ling Liu, Ka-Ho Chow +2
This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primaril…
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
Wenqi Wei, Ling Liu, Margaret Loper +4
Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients (edge devices). FL offers default client pr…
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
Ka-Ho Chow, Ling Liu, Mehmet Emre Gursoy +3
The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems. Real-time object recognition on the edge is one of t…