159 citations · 570 across the 11 of their papers we have counts for
11 papers
OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy
M. A. P. Chamikara, Seung Ick Jang, Ian Oppermann +9
Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus…
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches
Reena Zelenkova, Jack Swallow, M. A. P. Chamikara +5
Biometric data, such as face images, are often associated with sensitive information (e.g medical, financial, personal government records). Hence, a data breach in a system storing…
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…
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…
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)…
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…
A Fuzzy Based Model to Identify Printed Sinhala Characters (ICIAfS14)
G. I. Gunarathna, M. A. P. Chamikara, R. G. Ragel
Character recognition techniques for printed documents are widely used for English language. However, the systems that are implemented to recognize Asian languages struggle to incr…