All Outputs (3)

Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach (2020)
Presentation / Conference Contribution
Christou, O., Pitropakis, N., Papadopoulos, P., Mckeown, S., & Buchanan, W. J. (2020, February). Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach. Presented at ICISSP 2020, Valletta, Malta

Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate. Even with adequate training and high situational awareness, it can still be hard for users to continually be awa... Read More about Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach.

Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning (2020)
Presentation / Conference Contribution
Angelou, N., Benaissa, A., Cebere, B., Clark, W., Hall, A. J., Hoeh, M. A., …Titcombe, T. (2020, December). Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning. Poster presented at Neu

We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters th... Read More about Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning.

A Distributed Trust Framework for Privacy-Preserving Machine Learning (2020)
Presentation / Conference Contribution
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2020, September). A Distributed Trust Framework for Privacy-Preserving Machine Learning. Presented at The 17th International Conference on Trust, Privacy and Security in Digit

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 between data owners and data scientists. Data owners are justifiably reluct... Read More about A Distributed Trust Framework for Privacy-Preserving Machine Learning.