Security & Privacy
We are engaged in research and development of technologies enhancing security and privacy such as authentication, trust management, and privacy-preserving machine learning towards utilizing personal data in a trustworthy way. We have been involved in international standardization activities for authentication technologies with FIDO Alliance to advance identity management that ensures security and usability without the reliance on passwords. We also contribute to LY's services through on our research achievements such as differentially private federated learning for Sticker Auto-suggest in LINE app.
-
User-centric Identity Management
We are engaged in research and development of identity management technologies that allow users to appropriately handle their personal data while preserving their privacy by enhancing FIDO authentication. These technologies include PKaaS (Public-Key based authentication as a Service), which provides component functions as a service to enable identity providers to easily develop and operate a FIDO server and access control methods using verifiable credentials based on cryptographic proofs from FIDO authentication.
-
Privacy-preserving Machine Learning
We are also committed to the research and development of privacy-preserving machine learning technologies, striving to achieve a balance between adequate privacy considerations and personalized services for diverse users. Our research includes safer and more reliable federated learning and analytics, incorporating secure components such as differential privacy and secure computations.
Furthermore, we have published research on differentially private synthetic data generation techniques as a means to safely utilize sensitive data. This research field requires a comprehensive ability to utilize data, encompassing not only machine learning but also probabilistic data structures, cryptographic knowledge, and risk assessment.
