Digital Privacy and Security program

• privacy-preserving technologies
• confidential computing
• post-quantum safe solutions
• quantum safe communication
• enabling beneficial uses of sensitive data

• addressing core cybersecurity (software/ OS, enterprise, mobile and network security)
• anomaly and attack detection
• cyber-physical system security
• security compliance
• human centric cybersecurity
• challenges raised by AI and machine learning algorithms
• rapid threat actor identification without interception

• AI transparency and explainability, including aspects of interpretability, fairness and non-discrimination
• Digital Model Evaluation (DiME) and responsible AI (RAI) assessments for risks including accuracy, bias, and leakage of sensitive information

• design, implementation and cryptanalysis of cryptographic primitives, algorithms and protocols
• public key, quantum and post-quantum cryptography
• applications in secure and privacy-preserving systems and technologies, including quantum-secure communication and machine learning

• intelligent and explainable intrusion detection systems
• secure machine learning system development
• secure software engineering for machine learning-enabled systems
• malicious behaviour and anomaly detection

• human factors in cybersecurity
• malware analysis
• embedded systems security
• malicious behaviour detection in cloud-based activity
• usable cybersecurity (passwords, authentication)

• homomorphic encryption and secure multiparty computation
• biometric authentication
• secure and privacy-preserving machine learning, data analysis, cloud computing and machine un-learning
• security and privacy for emerging unconventional computing paradigms (fog computing, software-defined networking, 5G and 6G networks, quantum security)

• malicious behaviour detection in cloud-based activity
• effective public education of security concerns
• use of social media and social engineering to facilitate attacks

• applied AI in cybersecurity
• security orchestration, automation and response
• security testing, validation and measurements
• cyberphysical system security
• responsible AI considerations for detection of abusive online content
• responsible AI considerations for reducing race and gender bias in AI-enabled text analysis
• bias and accuracy assessment for AI models
• AI model security and data leakage assessment

Develop solutions to characterize and eliminate security gaps in future vehicular networks, with a special focus on privacy and quantum security to ensure reliability and performance for constrained communication networks.

Develop secure, efficient and privacy-aware authentication systems to strengthen cybersecurity in the aviation industry, including flight management systems, cabin crew devices and aircraft communications.

Deliver software for anomaly and attack detection and analysis in threat investigation, to improve the identification and prioritization of suspicious activity, increase detection rates and reduce false positives.

Develop an AI-powered decision support system for cyber threat analysis, capable of: monitoring the execution of complex distributed systems; identifying anomalous behaviours; diagnosing cause and effects; and providing intelligible threat reports and remediation strategies.

Generate an empirical framework to fill the gap between theoretical research and practical utilities in robust machine learning system development and security assurance, to meet the demands of trustworthy machine learning systems in areas such as smart airports and autonomous driving.