4 papers
Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
Chenxiang Zhang, Tongxi Qu, Zhong Li +3
Deep neural networks are widely deployed with quantization techniques to reduce memory and computational costs by lowering the numerical precision of their parameters. While quanti…
How does the optimizer implicitly bias the model merging loss landscape?
Chenxiang Zhang, Alexander Theus, Damien Teney +3
Model merging combines independent solutions with different capabilities into a single one while maintaining the same inference cost. Two popular approaches are linear interpolatio…
Spurious Privacy Leakage in Neural Networks
Chenxiang Zhang, Jun Pang, Sjouke Mauw
Neural networks trained on real-world data often exhibit biases while simultaneously being vulnerable to privacy attacks aimed at extracting sensitive information. Despite extensiv…
Proceedings 4th International Workshop on Engineering Safety and Security Systems
Jun Pang, Yang Liu, Sjouke Mauw
The present volume contains the proceedings of the Fourth International Workshop on Engineering Safety and Security Systems (ESSS'15). The workshop was held in Oslo, Norway, on Jun…