Honours and Minor Thesis projects

Verifiable Dynamic Searchable Symmetric Encryption (VDSSE) enables users to securely outsource databases (document sets) to cloud servers and perform searches and updates. The verifiability property prevents users from accepting incorrect search results returned by a malicious server. However, the community currently only focuses on preventing malicious behavior from the server but ignores incorrect updates from the client, which are very likely to happen in multi-user settings. Indeed most existing VDSSE schemes are not sufficient to tolerate incorrect updates from users. For instance,…

LLMs have enabled us to break the performance ceiling in several tasks. Language Agents took this further by connecting language models with tools, environments and enriched them with memory and feedback.

The increasing integration of Large Language Models (LLMs) into various sectors has recently brought to light the pressing need to align these models with human preferences and implement safeguards against the generation of inappropriate content. This challenge stems from both ethical considerations and practical demands for responsible AI usage. Ethically, there is a growing recognition that the outputs of LLMs must align with laws, societal values, and norms.

The last several years have witnessed the promising growth of AI-empowered techniques in mobile devices, from the camera to smart assistants. Users can find traces of AI in almost every aspect of mobile devices.

With the glow of digital information techniques, mobile systems are powerful ever and occupying more market shares. Just like wildly used social media sites, e.g. Facebook and Twitter, smartphone usage is up to 80% by 2020. In parallel to this trend, many companies are trying to incorporate Artificial Intelligent especially deep learning empowered applications into devices to further ease the life of people.

Graph neural networks (GNNs) are widely used in many applications. Their training graph data and the model itself are considered sensitive and face growing privacy threats.
 

Electricity is an essential part of modern life and the economy. Driven by a combination of policy support and rapidly falling costs of low-carbon technologies, Australia is experiencing a sharp rise in the deployment of distributed energy sources (DERs). Typical DERs include wind, solar photovoltaics (PV), battery storage, and electric vehicles (EVs) on the consumer side.

The rapid growth of electric vehicles (EVs) is transforming the transportation systems worldwide. Both EV fleets and private EVs are emerging as a cleaner and more sustainable component of urban mobility, forming an effective way to solve environmental problems and reduce commute costs in future smart cities. Due to the complex spatiotemporal behaviors of passengers and their travel patterns, the unmanaged electric charging demand from EVs may significantly impact the existing transportation and electrical power infrastructure.

Propositional satisfiability (SAT) is a well-known example of NP-complete problems. Although NP-completeness may be perceived as a drawback, it allows one to solve all the other problems in NP by reducing them to SAT and relying on the power of modern SAT solvers. This is confirmed by a wealth of successful examples of use cases for modern SAT solving, including generalisations and extensions of SAT as well as a wide variety of practical applications in artificial intelligence (AI).

Online fraud, also referred to as cyberscams, is increasingly becoming a cybersecurity problem that technical cybersecurity specialists are unable to effectively detect. Given the difficulty in the automatic detection of scams, the onus is often pushed back to humans to detect. Gamification and awareness campaigns are regularly researched and implemented in workplaces to prevent people from being tricked by scams, which may lead to identity theft or conning individuals out of money.