About SCRIPTS
What We Do
SCRIPTS specialises in privacy-preserving technologies (PP-Tech) that are computing techniques used to preserve individual’s privacy while allowing maximum value and insight to be extracted from available data. These technologies enable organisations to carry out data mining, analysis and sharing in compliance with data protection regulation enacted in various jurisdictions. PP-Tech will provide solutions and knowledge on how to best utilise data collected to benefit Singapore’s advancement towards a Smart Nation without compromising privacy.
The Centre focuses on the research, development and application of customised privacy-preserving technologies aligned with the national priorities of Singapore in the Services and Digital Economy of the Research, Innovation, and Enterprise (RIE) 2020 plan. Our work is funded by a $15.3 million grant from the Infocomm Media Development Authority (IMDA), and supported by the National Research Foundation, Singapore (NRF).
Research Focus
Learn moreWe explore the following PP-Tech with the aim of providing solutions to government and industry, adding value to the digital economy, and promoting social adoption.
Student Testimonial
I enrolled at NTU in 2022 to pursue a M.Eng degree under the supervision of Prof. Lam Kwok Yan. I am thrilled to share that I have recently graduated and started my next journey as a data scientist at a German company. I am deeply grateful for Prof. Lam’s unwavering support and thoughtful guidance. His mentorship provided me with an excellent opportunity to explore advanced topics, complemented by his invaluable insights, extensive knowledge, and vast experience. Under his supervision, I had the privilege of working in a conducive environment with ample time to delve into meaningful and impactful research. The dedication and rigorous approach to research that Prof. Lam instilled in me will remain a lifelong treasure and a cornerstone of my future career journey.
Shen Jiyuan, SCRIPTS Scholarship Recipient in AY2022/2023
Events
View all eventsSeminar by Assoc Prof Yuan Xingliang, University of Melbourne, Australia, RTP Harvard Room
Time: 17 Feb 2025, 2.00pm to 3.00pm
Venue: Research Techno Plaza (RTP), Level 2, Harvard Room
Title: Attacks and Defenses in Encrypted Search: Where Are We Now?
Bio: Xingliang Yuan is an Associate Professor in the School of Computing and Information Systems, the University of Melbourne. Prior to that, he was a faculty member at Monash University from 2017 to 2024. He has a keen interest in designing systems and protocols to address real-world security challenges. His current research focuses on data privacy, secure networked systems, and trustworthy machine learning. His research has been supported by Australian Research Council, CSIRO, Australian Department of Home Affairs, Australian Department of Health and Aged Care, and the Oceania Cyber Security Centre. His work has been published in major venues of computer security, such as CCS, S&P, USENIX Security, NDSS, TDSC, TIFS, etc. He is a sole recipient of the Dean's Award for Excellence in Research by an Early Career Researcher (2020), and the Faculty Teaching Excellence Award (2021) at Monash. He is on the editorial board of IEEE Transactions on Dependable and Secure Computing and IEEE Transactions on Service Computing. He is a program co-chair of Lamps@CCS'24, SecTL@AsiaCCS'23, NSS'22, a general co-chair of RAID'25, and a track co-chair of ICDCS'24, WISE'24, MSN'24. He is a senior member of IEEE, and a future fellow of Australian Research Council.
Abstract: The critical importance of protecting sensitive data is globally recognized. It is desired that sensitive data remains encrypted at all times—whether at rest, in transit, or in use. Achieving this goal is significantly challenging in the realm of modern databases. The core difficulty lies in enabling search over encrypted data, i.e., encrypted search, while balancing security, performance, and the ability to support diverse search functions. In this talk, I will provide a retrospective look at the evolution of encrypted search research, which has seen remarkable progress over the past decades. I will particularly focus on a recent focal point, i.e., leakage attacks and defenses in encrypted search, given its increasing deployment in the real-world. I will also pinpoint some on-going challenges and outline a roadmap for future research.
Patents
Lam Kwok Yan, et. al. Privacy-Preserving Neural Network Based on FHE Scheme, Fast Track Provisional Filing, 24 February 2022. Singapore provisional patent application number 10202201824W.
Lam Kwok Yan, et. al. Optimization Of FHE Scheme For Non-Linear Functions: An Efficient Design For LUT-Based Non-Linear Function Evaluation, Fast Track Provisional Filing, 13 May 2022. Singapore provisional patent application number 10202205037W.
Lam Kwok Yan, et. al. Privacy-Preserving Neural Network Model And Privacy-Preserving Prediction Using The Privacy-Preserving Neural Network Model, PCT Patent International Filing (claiming priority to earlier SG application nos. 10202201824W & 10202205037W), 15 February 2023, PCT Application No: PCT/SG2023/050085. International Publication No: WO 2023/163652 A2, 31 August 2023.
Lam Kwok Yan, et. al. Efficient FHE-Based Training On Encrypted Data, 23 January 2024, Singapore provisional patent application number 10202400198U.
Lam Kwok Yan, et. al. Two-Party Privacy-Preserving Subgraph Checking Over Unlabeled Graphs, 15 April 2024, Singapore provisional patent application number 10202401083W.