Social awareness is pivotal for those who work with data analytics and is a key factor that affects the uses, benefits, and risks of big data. It is a common practice for both government agencies and private entities to collect and integrate large amounts of many different kinds of data, process it in real time, and deliver the product or service to consumers. There are increasing worries that both the acquisition and subsequent application of big data analytics could cause various privacy breaches, render security concerns, enable discrimination, and negatively affect diversity in our society. All these concerns affect public trust regarding big data analytics and the ability of institutions to safeguard against such negative social outcomes. As such, social awareness should be an integral part of research and training in the area of data analytics. In this theme, we are focused on developing cutting-edge socially aware data analytics to address social concerns and meet laws and regulations in national-priority applications, thus better enabling big data analytics to promote social good and prevent social harm.
Our major research goals are to develop novel techniques to provide privacy preservation, fairness, safety, and robustness to a variety of data analytics and learning algorithms including automated data curation, social media and network analysis, and deep learning, and ensure the adoption of the developed techniques meet regulations, laws and user expectations.
Advancing the State of the Knowledge
Our developed technology can achieve meaningful and rigorous privacy protection when mining private data or collecting sensitive data from individuals; ensure non-discrimination, due process, and understandability in decision-making; achieve safe adoption, and robustness of machine learning and big data analytics techniques, especially in adversarial settings; and help incorporate social awareness in domain- or application-specific projects.
Our research projects in this theme will advance the state of the knowledge in the following perspectives.
- Privacy-preserving and attack resilient deep learning
- Socially aware crowdsourcing
- User-centric data sharing in cyberspaces
- Deep learning for preventing cross-media discrimination
- Marketing strategy design with fairness
- Privacy-preserving analytics in health and genomics
- Cryptography-assisted secure and privacy-preserving learning