396 citations · 1.6k across the 95 of their papers we have counts for
11 papers · 1 filter
Advancements of federated learning towards privacy preservation: from federated learning to split learning
Chandra Thapa, M. A. P. Chamikara, Seyit A. Camtepe
In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL) recently attracted much attention due to its applications in health, finance, and the lat…
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…
Evaluation of Federated Learning in Phishing Email Detection
Chandra Thapa, Jun Wen Tang, Alsharif Abuadbba +5
The use of Artificial Intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which opens it up to a myriad of privacy, trust, and l…
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…
Privacy Preserving Distributed Machine Learning with Federated Learning
M. A. P. Chamikara, P. Bertok, I. Khalil +2
Edge computing and distributed machine learning have advanced to a level that can revolutionize a particular organization. Distributed devices such as the Internet of Things (IoT)…
SplitFed: When Federated Learning Meets Split Learning
Chandra Thapa, M. A. P. Chamikara, Seyit Camtepe +1
Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learni…
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…