Portfolio

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Publications

Browsers’ Private Mode: Is It What We Were Promised?

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PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN

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Privacy and Trust Redefined in Federated Machine Learning

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A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric

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Privacy-Preserving Passive DNS

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Publication Links

• Browsers’ Private Mode: Is It What We Were Promised?
• Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure
• GLASS: Towards Secure and Decentralized eGovernance Services using IPFS
• PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform
• Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
• PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
• Practical Defences Against Model Inversion Attacks for Split Neural Networks
• Privacy and Trust Redefined in Federated Machine Learning
• A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric
• Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
• Privacy-Preserving Passive DNS
• Review and Critical Analysis of Privacy-preserving Infection Tracking and Contact Tracing
• A Distributed Trust Framework for Privacy Preserving Machine Learning
• Phishing URL Detection Through Top-level Domain Analysis: A Descriptive Approach

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