Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2020

Abstract

While decision-making task is critical in knowledge transfer, particularly from multi-source domains, existing knowledge transfer approaches are not generally designed to be privacy preserving. This has potential legal and financial implications, particularly in sensitive applications such as financial market forecasting. Therefore, in this paper, we propose a Privacy-preserving Multi-party Knowledge Transfer system (PMKT), based on decision trees, for financial market forecasting. Specifically, in PMKT, we leverage a cryptographic-based model sharing technique to securely outsource knowledge reflected in decision trees of multiple parties, and design a secure computation mechanism to facilitate privacy-preserving knowledge transfer. An encrypted user-submitted request from the target domain can also be sent to the cloud server for secure prediction. Also, the use of decision trees allows us to provide interpretability of the predictions. We then demonstrate how PMKT can achieve privacy guarantees, and empirically show that PMKT achieves accurate forecasting without compromising on accuracy.

Keywords

Decision tree, Financial market forecasting, Knowledge transfer, Multi-parties, Privacy-preserving, Secure computation

Discipline

Finance and Financial Management | Information Security

Research Areas

Cybersecurity

Publication

Future Generation Computer Systems

Volume

106

First Page

545

Last Page

558

ISSN

0167-739X

Identifier

10.1016/j.future.2020.01.007

Publisher

Elsevier

Embargo Period

1-7-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.future.2020.01.007

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