Research themes

Identity, as the core of blockchain eco-systems, has been widely drawn attention from businesses and research. However, the intellectual property and innovation in this area is still a vault to be discovered. Most messaging and transactions require authentication at the start, and the concern of privacy as well as information security can lead to the challenges of blockchain research. We plan to:

Step	Research areas
One	Develop identity digitalisation solutions and explore on-chain and off-chain patterns for privacy-preserving information storage.
Two	Develop next-generation encryption algorithms, consensus mechanisms and infrastructure for blockchain eco-systems.
Three	Develop innovative applications based on the research results on identity and traceability.

The distributed computing characteristics of blockchain provide a key link to effectively generate, use and mine big data from communities and IoT. With the pre-established trust mechanism and consensus model, users will have more control of their own data while sharing their data anonymously for incentives. We plan to:

Step	Research areas
One	Propose new approaches for digitalising physical assets, data acquisition, on-chain and off-chain data storage and processing.
Two	Practice in a selected industry and research on a new data exchange mechanism based on blockchain. Understand the characteristic of trusted data centre in an enterprise environment.
Three	Develop new algorithms and patterns on distributed ledger technology enabled big data framework.

In a trusted decentralised world, more and more communities with common characteristics are raising. In order to eliminate meaningless duplicates, the communication between side chain and main chain will be of crucial importance. We plan to:

Step	Research areas
One	Propose efficient communication and consensus mechanisms between side chain and main chain. Develop new methods to bridge and integrate the side chain and main chain data.
Two	Develop security strategies for the communication between side chain and main chain.
Three	Develop highly efficient interaction and communication mechanisms between multiple side chains and the main chain.

Federated learning is an emerging learning paradigm that enables privacy-preserving and efficient learning where local data is stored on local machines while the model updates are exchanged between the aggregators and devices. To improve the performances of federated learning, we plan to:

Step	Research areas
One	Propose a personalised incentive mechanism based on blockchain to encourage the devices with better data resources and higher computation power to participate in the learning process.
Two	Devise a blockchain-enabled verification mechanism to prevent from poisoning attacks, which is a leading attack in machine learning field.
Three	Develop a fully decentralised federated learning paradigm that allows each machine to be the potential aggregation in different rounds, which prevents single point failure and insider attacks.

Blockchain-related information search is encountered in many practical applications. Several current research directions include blockchain-aided search over paid databases, blockchain-aided secure keyword storage and search, enhanced search for data stored in blockchain. To achieve high-performance information search in blockchain, we plan to:

Step	Research areas
One	Realise search optimisation in blockchain since blockchain is growing ever massive, so is the amount of data stored in it.
Two	Design a novel indexing method which enables efficient and accurate search in blockchain.
Three	Develop a generalised information search platform that integrates existing blockchain systems.