Privacy Attacks on Network Embeddings
Authors: Michael Ellers, Michael Cochez, Tobias Schumacher, Markus Strohmaier, Florian Lemmerich | Published: 2019-12-23
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Adversarial Learning of Privacy-Preserving and Task-Oriented Representations
Authors: Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker, Ming-Hsuan Yang | Published: 2019-11-22
Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability
Authors: Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Wenqi Wei, Lei Yu | Published: 2019-11-21
Privacy Leakage Avoidance with Switching Ensembles
Authors: Rauf Izmailov, Peter Lin, Chris Mesterharm, Samyadeep Basu | Published: 2019-11-18
MaskedNet: The First Hardware Inference Engine Aiming Power Side-Channel Protection
Authors: Anuj Dubey, Rosario Cammarota, Aydin Aysu | Published: 2019-10-29 | Updated: 2019-12-02
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text
Authors: Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake | Published: 2019-10-20
Sequence embeddings help to identify fraudulent cases in healthcare insurance
Authors: I. Fursov, A. Zaytsev, R. Khasyanov, M. Spindler, E. Burnaev | Published: 2019-10-07
Piracy Resistant Watermarks for Deep Neural Networks
Authors: Huiying Li, Emily Wenger, Shawn Shan, Ben Y. Zhao, Haitao Zheng | Published: 2019-10-02 | Updated: 2020-12-02
Alleviating Privacy Attacks via Causal Learning
Authors: Shruti Tople, Amit Sharma, Aditya Nori | Published: 2019-09-27 | Updated: 2020-07-17
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges
Authors: Jinyuan Jia, Neil Zhenqiang Gong | Published: 2019-09-17 | Updated: 2019-09-19