Ant Group and Nanyang Technological University, Singapore (NTU Singapore) have begun a research collaboration to explore privacy-preserving computing technology innovations.
What is the mission behind the collaboration?
The one-year-long collaboration will focus on Private Set Intersection (PSI), a cryptographic protocol that allows parties to compute the intersection of their private datasets without revealing anything beyond the intersection, to ensure the privacy of all parties involved.
The collaboration aims to increase the practical capability and security of PSI, optimize its efficiency, and develop applications for real-world use cases. NTU Singapore’s Strategic Centre for Research in Privacy-Preserving Technologies & Systems (SCRIPTS) and its global expertise in privacy-preserving computing, combined with Ant Group’s R&D capabilities, and extensive industry insights, are expected to push the frontiers of this emerging field.
“We are excited to join hands with NTU Singapore to promote research into privacy-preserving computing technologies, including PSI, and support technical innovations in Singapore,” said Jerry Yin, Chief Technology Officer of International Business at Ant Group.
“We believe that this university-industry collaborated research will help accelerate the development of privacy-preserving computing, and pave the way for new applications to unlock greater value in the digitalization of industry collaboration,” Jerry Yin further said.
What does this mean for Ant Group?
Ant Group has been exploring technological innovation in privacy-preserving computing in recent years. According to IPR Daily and incoPat, Ant Group topped the list of patent applications for privacy-preserving computing tech in 2022, with 1,152 filed applications.
In July 2022, Ant Group announced the open source of its privacy-preserving computation framework, with the primary goal of making the revolutionary technologies more accessible to developers across the planet and to accelerate its application in various scenarios.
Since its launch in 2016, the framework has integrated comprehensive privacy-preserving tech like multi-party secure computation, federated learning, homomorphic encryption, differential privacy, zero-knowledge proof, and trusted execution environment, and has been implemented in multiple use cases including healthcare, insurance, and risk management.
