Primary Focus Area: Privacy and Security in AI
Secondary Focus Areas: Adaptation of Foundation Models; AI for Cybersecurity
Abstract:
This RBO aims to prototype secure, cloud-based AI workflows using privacy-preserving computation methods like secure multi-party computation (MPC), federated learning, and trusted execution environments (TEEs). These technologies can enable sensitive data to be used in training and inference while ensuring confidentiality across stakeholders. The project will benchmark the feasibility of these tools under real-world performance constraints and assess their security benefits, targeting high-impact use cases in e-governance, healthcare, and cybersecurity.
Gap:
While MPC and other privacy-enhancing technologies (PETs) offer strong security, they are rarely applied in large-scale AI systems due to performance, deployment, and integration challenges. TEEs are gaining traction but bring new attack surfaces. Federated learning helps distribute learning but may still leak sensitive information. Current research lacks practical guidance on when and how these tools can be effectively used in full AI pipelines, particularly in cloud-based settings with strict privacy and regulatory requirements.
Objective:
Design and prototype end-to-end secure AI workflows, integrating MPC, federated learning, and other secure computing methods. Evaluate their performance, scalability, and risk reduction compared to standard techniques. The outcome will be tested prototypes and a comparative risk analysis demonstrating improved privacy preservation.
Approach:
- Task 1: MPC blueprint for model updating and inference; optimise protocols and investigate MPC-specific ML algorithms
- Task 2: Federated learning with MPC, including encrypted backpropagation and model aggregation; test various deployment models
- Prototypes: Implement, benchmark, and analyse deployments for feasibility and risk
- Deployment focus: Support edge/cloud scenarios; develop full encrypted training/inference pipelines depending on security needs
Impact
Secure computing (especially MPC) enables joint data analysis without exposing individual values—critical for sectors with data-sharing restrictions. This RBO will optimise secure deployments for ML, offering solutions that meet privacy, compliance, and IP protection needs.