5 papers · 1 filter
Cooperative Local Differential Privacy: Securing Time Series Data in Distributed Environments
Bikash Chandra Singh, Md Jakir Hossain, Rafael Diaz +3
The rapid growth of smart devices such as phones, wearables, IoT sensors, and connected vehicles has led to an explosion of continuous time series data that offers valuable insight…
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Sayyed Farid Ahamed, Sandip Roy, Soumya Banerjee +6
Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extract…
RADEP: A Resilient Adaptive Defense Framework Against Model Extraction Attacks
Amit Chakraborty, Sayyed Farid Ahamed, Sandip Roy +6
Machine Learning as a Service (MLaaS) enables users to leverage powerful machine learning models through cloud-based APIs, offering scalability and ease of deployment. However, the…
RESTRAIN: Reinforcement Learning-Based Secure Framework for Trigger-Action IoT Environment
Md Morshed Alam, Lokesh Chandra Das, Sandip Roy +2
Internet of Things (IoT) platforms with trigger-action capability allow event conditions to trigger actions in IoT devices autonomously by creating a chain of interactions. Adversa…
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning
Soumya Banerjee, Sandip Roy, Sayyed Farid Ahamed +7
The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to over…