193 citations · 508 across the 15 of their papers we have counts for
15 papers
Defending Against Beta Poisoning Attacks in Machine Learning Models
Nilufer Gulciftci, M. Emre Gursoy
Poisoning attacks, in which an attacker adversarially manipulates the training dataset of a machine learning (ML) model, pose a significant threat to ML security. Beta Poisoning is…
Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Alireza Khodaie, Berkay Kemal Balioglu, Mehmet Emre Gursoy
Local differential privacy (LDP) has recently gained prominence as a powerful paradigm for collecting and analyzing sensitive data from users' devices. However, the inherent pertur…
Grid-Based Decompositions for Spatial Data under Local Differential Privacy
Berkay Kemal Balioglu, Alireza Khodaie, Ameer Taweel +1
Local differential privacy (LDP) has recently emerged as a popular privacy standard. With the growing popularity of LDP, several recent works have applied LDP to spatial data, and…
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…
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…
Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability
Stacey Truex, Ling Liu, Mehmet Emre Gursoy +2
Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evalu…
Utility-aware and privacy-preserving mobile query services
Emre Yigitoglu, Mehmet Emre Gursoy, Ling Liu
Location-based queries enable fundamental services for mobile road network travelers. While the benefits of location-based services (LBS) are numerous, exposure of mobile travelers…
Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy
Mehmet Emre Gursoy, Acar Tamersoy, Stacey Truex +2
Local Differential Privacy (LDP) is popularly used in practice for privacy-preserving data collection. Although existing LDP protocols offer high utility for large user populations…
Differentially Private Model Publishing for Deep Learning
Lei Yu, Ling Liu, Calton Pu +2
Deep learning techniques based on neural networks have shown significant success in a wide range of AI tasks. Large-scale training datasets are one of the critical factors for thei…
Towards Demystifying Membership Inference Attacks
Stacey Truex, Ling Liu, Mehmet Emre Gursoy +2
Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service…
Adversarial Examples in Deep Learning: Characterization and Divergence
Wenqi Wei, Ling Liu, Margaret Loper +4
The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data. Adversarial attacks in deep learnin…