Privacy-Preserving Collaborative Analytics on Sensitive Data

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This project aims to develop efficient solutions that allow multiple institutes to carry out collaborative analytics on aggregated data without revealing their sensitive data to each other. The project expects to remedy acute privacy concerns when institutes share sensitive data across boundaries for collective insights. The expected outcomes include a hybrid trust model with distributed trusts to provide malicious security guarantees, lightweight privacy-enhancing techniques to express rich analytical functionalities, and a system platform for real-world applications. This should provide significant benefits such as facilitating industries to safeguard their customers' data and uplift their businesses in a secure and trustworthy fashion.