Privacy-preserving Surveillance Methods using Homomorphic Encryption
Conference Proceeding
Bowditch, W., Abramson, W., Buchanan, W. J., Pitropakis, N., & Hall, A. J. (2020)
Privacy-preserving Surveillance Methods using Homomorphic Encryption. In ICISSP: Proceedings of the 6th International Conference on Information Systems Security and Privacy. , (240-248). https://doi.org/10.5220/0008864902400248
Data analysis and machine learning methods often involve the processing of cleartext data, and where this could breach the rights to privacy. Increasingly, we must use encrypt...
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier
Conference Proceeding
Hall, A. J., Pitropakis, N., Buchanan, W. J., & Moradpoor, N. (2019)
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier. In 2018 IEEE International Conference on Big Data (Big Data)https://doi.org/10.1109/BigData.2018.8621922
Insider threats continue to present a major challenge for the information security community. Despite constant research taking place in this area; a substantial gap still exis...
Privacy and Trust Redefined in Federated Machine Learning
Journal Article
Papadopoulos, P., Abramson, W., Hall, A. J., Pitropakis, N., & Buchanan, W. J. (2021)
Privacy and Trust Redefined in Federated Machine Learning. Machine Learning and Knowledge Extraction, 3(2), 333-356. https://doi.org/10.3390/make3020017
A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthc...
A Distributed Trust Framework for Privacy-Preserving Machine Learning
Conference Proceeding
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2020)
A Distributed Trust Framework for Privacy-Preserving Machine Learning. In Trust, Privacy and Security in Digital Business. , (205-220). https://doi.org/10.1007/978-3-030-58986-8_14
When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust ...