79 citations · 163 across the 14 of their papers we have counts for
14 papers
SePEnTra: A secure and privacy-preserving energy trading mechanisms in transactive energy market
Rumpa Dasgupta, Amin Sakzad, Carsten Rudolph +1
In this paper, we design and present a novel model called SePEnTra to ensure the security and privacy of energy data while sharing with other entities during energy trading to dete…
Email Summarization to Assist Users in Phishing Identification
Amir Kashapov, Tingmin Wu, Alsharif Abuadbba +1
Cyber-phishing attacks recently became more precise, targeted, and tailored by training data to activate only in the presence of specific information or cues. They are adaptable to…
A Blockchain-Based Consent Mechanism for Access to Fitness Data in the Healthcare Context
May Alhajri, Carsten Rudolph, Ahmad Salehi Shahraki
Wearable fitness devices are widely used to track an individual's health and physical activities to improve the quality of health services. These devices sense a considerable amoun…
Privacy of Fitness Applications and Consent Management in Blockchain
May Alhajri, Ahmad Salehi Shahraki, Carsten Rudolph
The rapid advances in fitness wearable devices are redefining privacy around interactions. Fitness wearables devices record a considerable amount of sensitive and private details a…
Authentication and Access Control in 5G Device-to-Device Communication
Jithu Geevargheese Panicker, Ahmad Salehi S., Carsten Rudolph
Device-to-device (D2D) communication is one of the most recent advancements in wireless communication technology. It was introduced in cellular communication technology by the 3rd…
Decentralized Policy Information Points for Multi-Domain Environments
M Ridwanur Rahman, Ahmad Salehi Shahraki, Carsten Rudolph
Access control models have been developed to control authorized access to sensitive resources. This control of access is important as there is now a need for collaborative resource…
Robust Training Using Natural Transformation
Shuo Wang, Lingjuan Lyu, Surya Nepal +3
Previous robustness approaches for deep learning models such as data augmentation techniques via data transformation or adversarial training cannot capture real-world variations th…
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning
Shuo Wang, Surya Nepal, Kristen Moore +3
The diversity and quantity of data warehouses, gathering data from distributed devices such as mobile devices, can enhance the success and robustness of machine learning algorithms…
Adversarial Defense by Latent Style Transformations
Shuo Wang, Surya Nepal, Alsharif Abuadbba +2
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack…
Defending Adversarial Attacks via Semantic Feature Manipulation
Shuo Wang, Tianle Chen, Surya Nepal +3
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and…
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Shuo Wang, Tianle Chen, Shangyu Chen +3
Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as…
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
Shuo Wang, Surya Nepal, Carsten Rudolph +3
Transfer learning provides an effective solution for feasibly and fast customize accurate \textit{Student} models, by transferring the learned knowledge of pre-trained \textit{Teac…
Generating Semantic Adversarial Examples via Feature Manipulation
Shuo Wang, Surya Nepal, Carsten Rudolph +3
The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wi…
Defeating Misclassification Attacks Against Transfer Learning
Bang Wu, Shuo Wang, Xingliang Yuan +3
Transfer learning is prevalent as a technique to efficiently generate new models (Student models) based on the knowledge transferred from a pre-trained model (Teacher model). Howev…