We advance both the foundations and practice of cybersecurity. Our work spans cryptography, distributed systems, privacy-preserving technologies, software, and the security of AI. By combining mathematical foundations with advances in Artificial Intelligence, we address security challenges from fundamental protocols to large-scale infrastructure. These innovations enable us to protect sensitive information, secure digital supply-chain, increase accountability, and build trust in critical systems across finance, healthcare, government, and beyond.
Our research spans across multidisciplinary research
Our research expands from theoretical foundation of modern cryptography, such as cryptography secure against backdoor, to applied cryptography with versatile applications in end to end encrypted Internet services, secure and verifiable outsourced computation, distributed and timed cryptography, and to the emerging challenges of post quantum transition.
Cryptography is the backbone of communication and computation security, providing protection at the data layer. Those research enable secure and efficient data sharing, data analytics, and collaborative data processing, as well as secure data supply chain, particularly needed in the quantum and AI era.
Associate Professor Qiang Tang, Dr Sri AravindaKrishnan Thyagarajan
Our research targets the most fundamental distributed system security problems including (fast and verified) consensus protocols under realistic Internet environments, and with advanced properties such as order fairness, distributed randomness and key generation protocols, threshold cryptography, secure multiparty computation, distributed database, federated learning and more.
Distributed system provides system resilience and availability, even under cyber attacks or partial failures. Those are fundamental to critical infrastructure, from cloud services and financial networks to AI pipelines. Distributed protocols also facilitate new generations of systems and applications such as DeFi, DAO and more.
Professor Vincent Gramoli, Dr Qiang Tang, Dr Sri AravindaKrishnan Thyagarajan, Dr Jiangshan Yu, Professor Albert Zomaya
Our research reimagines cybersecurity in the age of artificial intelligence. We develop autonomous and AI assisted systems that can discover vulnerabilities, reason about complex software and infrastructure, detect emerging threats, and respond to attacks with minimal human intervention. By combining large language models, intelligent agents, machine learning, automated reasoning, and program analysis, we aim to transform security from a largely reactive process into a proactive and continuously adapting discipline. Our work spans software security, cyber defence, blockchain ecosystems, steganography and copyright protection, critical infrastructure, and next generation digital systems, with the goal of building security capabilities that operate at machine speed and planetary scale.
More effective and scalable security solutions for software, networks, cloud systems, critical infrastructure, and blockchain ecosystems. This research helps security teams identify vulnerabilities faster, respond to threats more effectively, reduce manual effort, and improve the resilience of digital systems against increasingly sophisticated attacks.
Dr Liyi Zhou, Associate Professor Suranga Seneviratne, Associate Professor Qiang Tang, Professor Albert Zomaya, Dr Hong Jin Kang
Artificial intelligence is rapidly becoming a foundational layer of society, yet its security, reliability, and trustworthiness remain largely unsolved challenges. Our research seeks to establish the scientific foundations for secure AI systems that can be safely deployed in high impact environments. We study how AI models, autonomous agents, and AI powered decision making systems behave under adversarial conditions, interact with external tools and infrastructure, and make complex decisions at scale. Our work develops new methods for securing AI against manipulation, misuse, privacy violations, and unexpected behaviour, while advancing techniques for verification, interpretability, and trustworthy autonomy. Ultimately, we aim to build AI systems that remain reliable, aligned, and resilient even in open and adversarial environments.
Safer and more trustworthy AI systems across healthcare, finance, government, education, and critical digital infrastructure. This research helps reduce risks from insecure or unreliable AI systems, supports responsible deployment of intelligent technologies, and improves public trust in AI driven services and decision making.
Associate Professor Suranga Seneviratne, Associate Professor Qiang Tang, Dr Liyi Zhou, Professor Albert Zomaya
Our research builds more secure, resilient software systems in a world increasingly dependent on digital infrastructure. We tackle vulnerability detection, mitigation & repair, and attack detection and mitigation spanning secure coding practices, supply chain security, and automated analysis tools.
We also examine the security implications of emerging technologies including AI, cloud platforms, and IoT devices.
Stronger digital security across healthcare, finance, government, and critical infrastructure. This leads to lower risk in online transactions, and safer online experiences for everyone.
Dr Rahul Gopinath, Dr Hong Jin Kang, Dr Xi Wu, Dr Liyi Zhou
Our research tackles the challenge of privacy-preserving protocols among untrustworthy parties, such as private information retrieval, private payments, private exchange anonymous communication and others, that facilitate collaboration and computation without leaking actual data (or identity); as well as differential privacy, revealing aggregate insights from datasets while fully protecting individual privacy, in statics and VR systems.
Privacy-preserving protocols allow parties to collaborate without revealing private inputs, underpinning secure healthcare analytics, federated AI, and confidential financial systems. Differential privacy provides mathematically rigorous guarantees against individual data inference, with landmark deployments at Apple, Google, and the US Census Bureau — making privacy a technical guarantee rather than a policy promise.
Dr Clément Canonne, Associate Professor Kanchana Thilakarathna, Associate Professor Qiang Tang, Dr Sri AravindaKrishnan Thyagarajan, Dr Jiangshan Yu
Our research designs technical methods that enforce good behaviors beyond soft mechanisms only via compliance, regulation and policy. This includes accountability in consensus protocols, proactive deterring mechanisms for non-sharing policy in public key infrastructure and digital rights management, private payments and exchange systems for compliance, and more.
As digital infrastructure grows in scale and complexity, accountability research delivers a critical capability across industries: cryptographic proof that participants behaved honestly, and verifiable evidence when they did not. From cloud service providers and financial platforms to healthcare systems and collaborative enterprise software, accountability mechanisms turn security violations from undetectable events into publicly provable facts — reducing fraud, strengthening trust, enabling regulatory compliance, and providing the auditability that modern digital systems increasingly demand.
Professor Vincent Gramoli, Associate Professor Qiang Tang, Dr Sri AravindaKrishnan Thyagarajan
