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 Professor Wang Yan, Macquarie University, Australia, RTP Harvard Room
Time: 23 Jan 2025, 3.00pm to 4.00pm
Venue: Research Techno Plaza (RTP), Level 2, Harvard Room
Title: Utilizing Recommender System Techniques to Fight Fake News
Bio: Dr. Yan Wang is currently a Full Professor in the School of Computing, Macquarie University, Australia. He received his PhD from Harbin Institute of Technology (HIT), P. R. China. Prior to joining Macquarie University in 2003, he worked as a Postdoctoral Fellow/Research Fellow in the Department of Computer Science, School of Computing, National University of Singapore (NUS). He has published a number of research papers in international conferences including AAAI, AAMAS, ICDE, IJCAI, KDD, NeurIPS, SIGIR, WWW, and journals including CSUR, TIST, TKDD, TKDE, TSC and TWEB. His research interests cover recommender systems, trust management, social computing and service computing.
Prof. Wang has served on the editorial board of several international journals, including IEEE Transactions on Services Computing (TSC), Service-Oriented Computing & Applications (SOCA) by Springer, and Human-centric Computing and Information Sciences (HCIS) by Springer. He also served as a General co-Chair of IEEE ATC2013, IEEE ATC2014, IEEE MS2015, IEEE ICWS2016 and IEEE CLOUD2017, a Program co-Chair of IEEE SCC2011, ATC2011, IEEE MS2014, IEEE SCC2018, IEEE SOSE2018 and IEEE SCC2019, and a Local Organisation Chair of IEEE DSAA2020.
Prof. Wang's research team has received a number of awards, including four Best (Student) Paper Awards from IEEE SCC2010, IEEE TrustCom2012, IEEE ICWS2016 and IEEE DSAA2024 respectively, Vice-Chancellor's (University President) Commendation for Academic Excellence in PhD thesis for 4 times (2017, 2020, 2020, 2024), and the nomination for Australasian Distinguished Doctoral Dissertation for 3 times. Prof. Wang received 2017 IEEE TC-TVSC Outstanding Service Award from the IEEE Technical Committee on Services Computing (TC-SVC), IEEE Computer Society.
Abstract: Recommender System aims to predict to what extent a user may like an item based on historical data. In addition, fake news detection and mitigation are long-term challenging tasks. This talk will introduce some studies that utilize recommender system techniques to fight fake news.
First, this talk will introduce a novel solution, which is the first in the literature to introduce recommender system technique to fake news mitigation. The proposed solution can differentiate the events behind news, identify the veracity of news, and recommend true news to users based on their historical data. Second, this talk will introduce a novel solution for unbiased and true news recommendation. It can not only capture users’ high- and low-level interests, enhancing next-news recommendation accuracy, but also effectively separate polarity and veracity information from news contents and model them more specifically, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations.
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.