396 citations · 1.6k across the 95 of their papers we have counts for
7 papers · 2 filters
DeFuzz: Deep Learning Guided Directed Fuzzing
Xiaogang Zhu, Shigang Liu, Xian Li +4
Fuzzing is one of the most effective technique to identify potential software vulnerabilities. Most of the fuzzers aim to improve the code coverage, and there is lack of directedne…
Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy
Chandra Thapa, Seyit Camtepe
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personal…
PPaaS: Privacy Preservation as a Service
Pathum Chamikara Mahawaga Arachchige, Peter Bertok, Ibrahim Khalil +2
Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-…
An Empirical Assessment of Global COVID-19 Contact Tracing Applications
Ruoxi Sun, Wei Wang, Minhui Xue +3
The rapid spread of COVID-19 has made manual contact tracing difficult. Thus, various public health authorities have experimented with automatic contact tracing using mobile applic…
Privacy Preserving Face Recognition Utilizing Differential Privacy
M. A. P. Chamikara, P. Bertok, I. Khalil +2
Facial recognition technologies are implemented in many areas, including but not limited to, citizen surveillance, crime control, activity monitoring, and facial expression evaluat…
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
Sharif Abuadbba, Kyuyeon Kim, Minki Kim +5
A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively r…
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
Yansong Gao, Minki Kim, Sharif Abuadbba +6
This work is the first attempt to evaluate and compare felderated learning (FL) and split neural networks (SplitNN) in real-world IoT settings in terms of learning performance and…