193 citations · 591 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples
Wenqi Wei, Ling Liu, Margaret Loper +4
Deep neural network (DNN) has demonstrated its success in multiple domains. However, DNN models are inherently vulnerable to adversarial examples, which are generated by adding adv…
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
Ling Liu, Wenqi Wei, Ka-Ho Chow +4
Ensemble learning is a methodology that integrates multiple DNN learners for improving prediction performance of individual learners. Diversity is greater when the errors of the en…
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…