29 Apr 2021 03:00pm - 30 Apr 2021 05:00pm

CUHK x ASTRI Industry Talk Series: Data Analytics vs. Data Privacy, Synergy or Compromise?

Date: 29 Apr 2021 03:00pm - 30 Apr 2021 05:00pm

Type: Seminar 

Host: Others 

Venue | Live streaming at InnoPort, CUHK

Speaker / Guests of Honour
Dr. Shum Kam Hong
Director of Applied Cryptosystems, Cybersecurity, Cryptography & Trusted Technology Division in Hong Kong Applied Science and Technology Research Institute (ASTRI)

Prof. Sherman Chow
Department of Information Engineering, CUHK

Language: English

Register Now

Rundown

Privacy Preservation – The Critical Building Block of Fintech Applications
by Dr. Shum Kam Hong, Director of Applied Cryptosystems, Cybersecurity, Cryptography & Trusted Technology Division in Hong Kong Applied Science and Technology Research Institute (ASTRI)

Can We Securely Outsource Big Data Analytics with Lightweight Cryptography?
by Prof. Sherman Chow, Department of Information Engineering, CUHK

Panel Discussion moderated by Prof. Raymond Tsang, Centre for Financial Engineering, CUHK

Q&A Session

Abstract:

Privacy Preservation – The Critical Building Block of Fintech Applications

Although the potential benefits of sharing enterprises’ data to perform AI and machine learning algorithms are huge, the issue of data privacy prohibits the realization of data sharing. This talk will first describe different privacy preserving technologies that support data sharing without compromising the protection of data privacy. Among these technologies, Federated Learning is highly promising in empowering many innovative applications by running machine learning models on data originating from different data providers. The utilization of such data, known as alternative data, will not be possible without the proper implementation of privacy preserving technologies. The use cases of using alternative data by fintech applications will be compared and described in this talk.

Can We Securely Outsource Big Data Analytics with Lightweight Cryptography?

Machine learning often involves sensitive data. It is desirable to preserve all stakeholders’ privacy, namely, querying clients, model owner, and training data contributors. State-of-the-art cryptographic solutions are still orders of magnitudes slower than plaintext training and inference. To narrow the gap between theory and practice, we put forward a new paradigm for privacy-preserving big data analytics that leverages GPU (Graphics Processing Unit) and trusted processor (such as Intel SGX). Due to the memory constraint of SGX and the untrusted nature of GPU, we still need specific cryptographic techniques to make it secure and practical. This talk will share some recent results published in AAAI, IJCAI, NDSS, and Usenix Security from our cryptography research group at the Department of Information Engineering, CUHK.

Speakers:

Dr. KH Shum has over 25 years of working experience in both the industry and academia. He is currently the Director of Applied Cryptosystems, Cybersecurity, Cryptography & Trusted Technology Division in Hong Kong Applied Science and Technology Research Institute (ASTRI). His experience in the IT industry includes CTO and technical director positions in various IT companies, specializing in the areas of security, e-payment, and fintech solutions. The IT systems designed by him had been deployed widely in Singapore, Hong Kong, Korea, Japan, Thailand and other countries in Asia.

Dr. Shum has been conducting research in the areas of cryptography and fintech security since the late nineties. He is a former faculty member of the National University of Singapore and the Singapore Management University. He received his PhD degree in computer science from the University of Cambridge with the support of the scholarship from the Hong Kong Croucher Foundation. He has also recently received the degree of Doctor of Education from the University of Hong Kong, specializing in the use of data analytics in learning technologies.

Prof. Sherman Chow got his Ph.D. from Courant Institute of Mathematical Sciences, New York University. He publishes in and serves as a program committee of many top-tier conferences in cryptography and security, including AsiaCrypt, CCS, and Usenix Security, and fintech and privacy conferences, such as PETS and Financial Cryptography. He is the Deputy Editor in Cryptography of IET Information Security, and served on the award committee of The Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2019. He is a European Alliance for Innovation (EAI) Fellow (2019, inaugural), and named as one of the 100 Most Influential Scholars (Security and Privacy, 2018) by ArnetMiner (AMiner). He is also a founding member of IEEE SMC’s Blockchain technical committee and a research committee of the Hong Kong Blockchain Society.