1 citations · 1 across the 2 of their papers we have counts for
4 papers
Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks
Ayana Moshruba, Hamed Poursiami, Maryam Parsa
Biological neurons exhibit diverse temporal spike patterns, which are believed to support efficient, robust, and adaptive neural information processing. While models such as Izhike…
On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels
Ayana Moshruba, Shay Snyder, Hamed Poursiami +1
As machine learning models increasingly process sensitive data, understanding their vulnerability to privacy attacks is vital. Membership inference attacks (MIAs) exploit model res…
Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
Hamed Poursiami, Ayana Moshruba, Maryam Parsa
As machine learning models become integral to security-sensitive applications, concerns over data leakage from adversarial attacks continue to rise. Model Inversion (MI) attacks po…
Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study
Ayana Moshruba, Ihsen Alouani, Maryam Parsa
While machine learning (ML) models are becoming mainstream, especially in sensitive application areas, the risk of data leakage has become a growing concern. Attacks like membershi…