242 citations · 809 across the 8 of their papers we have counts for
8 papers
Local Differential Privacy for Federated Learning
M. A. P. Chamikara, Dongxi Liu, Seyit Camtepe +4
Advanced adversarial attacks such as membership inference and model memorization can make federated learning (FL) vulnerable and potentially leak sensitive private data. Local diff…
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-…
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)…
Local Differential Privacy for Deep Learning
M. A. P. Chamikara, P. Bertok, I. Khalil +3
The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continual…
An Efficient and Scalable Privacy Preserving Algorithm for Big Data and Data Streams
M. A. P. Chamikara, P. Bertok, D. Liu +2
A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources,…
Efficient privacy preservation of big data for accurate data mining
M. A. P. Chamikara, P. Bertok, D. Liu +2
Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that potential…
Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining
M. A. P. Chamikara, P. Bertok, D. Liu +2
The widespread use of the Internet of Things (IoT) has raised many concerns, including the protection of private information. Existing privacy preservation methods cannot provide a…