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Project Database
Project Reference: ITP/006/22LP
Project Title: Efficient Logistic Regression on Encrypted Data for Privacy Preserving Data Sharing
Hosting Institution: LSCM R&D Centre (LSCM)
Abstract: With the increasing adoption of big data technologies, artificial intelligence, and the
Internet of Things, data sharing becomes crucial for organizations across the public and
private sectors globally. Gartner, in the article "Data is a Business Necessity to
Accelerate Digital Business", predicts that by 2023, organizations that promote data
sharing will outperform those who do not on most business value metrics. Data sharing
can also help to deal with some of the society’s biggest challenges more innovatively
and effectively.
One of the major difficulties data trading platforms face is to protect data owners' interests
by maintaining appropriate level of privacy and confidentiality through encryption
technology and still can perform regulatory compliance due diligence check, and data
mining activities to generate valuable insights. Logistic regression is one of the most
common machine learning methods being used in the plaintext scenario to achieve these
objectives. Baldwin and Krishna Dayanidhi in the book "Natural Language Processing
with Java and LingPipe Cookbook", points out that logistic regression is one of the
machine learning methods that is responsible for the majority of industrial classifier and is
most certainly one of the best performing classifiers available. Furthermore, a neural
network can be viewed as a series of logistic regression classifiers stacked on top of
each other (see "Speech and Language Processing, 3rd edition, by Dainiel Jurasky and
James Martin). We therefore propose to develop and implement an efficient method to
perform logistic regression on encrypted data with appropriate accuracy and precision.
This provides a foundation for the development of a privacy-preserving data sharing
platform that can perform appropriate due diligence check and generate valuable
insights. Furthermore, since all data remain encrypted throughout the process,
confidentiality and privacy are achieved through encryption. The platform can also act as
a privacy preserving data mining service provider to data owners.
Project Coordinator: Dr Russell Siu Wai Yiu
Approved Funding Amount: HK$ 2.76 M
Project Period: 31 Mar 2022 - 30 Mar 2023
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