Privacy and Trust Redefined in Federated Machine Learning
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), 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...
Privacy-preserving Surveillance Methods using Homomorphic Encryption
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...
A Distributed Trust Framework for Privacy-Preserving Machine Learning
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 ...
Trust-by-Design: Evaluating Issues and Perceptions within Clinical Passporting
Abramson, W., van Deursen, N. E., & Buchanan, W. J. (2020)
Trust-by-Design: Evaluating Issues and Perceptions within Clinical Passporting. Blockchain in Healthcare Today, 3, https://doi.org/10.30953/bhty.v3.140
A substantial administrative burden is placed on healthcare professionals as they manage and progress through their careers. Identity verification, pre-employment screening an...