159 citations · 260 across the 5 of their papers we have counts for
10 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…