DAG: A General Model for Privacy-Preserving Data Mining

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DAG: A General Model for Privacy-Preserving Data Mining
Title:
DAG: A General Model for Privacy-Preserving Data Mining
Journal Title:
IEEE Transactions on Knowledge and Data Engineering
Publication Date:
12 November 2018
Citation:
S. G. Teo, J. Cao and V. C. S. Lee, "DAG: A General Model for Privacy-Preserving Data Mining," in IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 1, pp. 40-53, 1 Jan. 2020, doi: 10.1109/TKDE.2018.2880743.
Abstract:
Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. SMC has been extensively applied in tasks with privacy requirements, such as privacy-preserving data mining (PPDM), to learn task output and at the same time protect input data privacy. However, existing SMC-based solutions are ad-hoc – they are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed Acyclic Graph) that consists of a set of fundamental secure operators (e.g., +, -, *, /, and power). Our model is general – its operators, if pipelined together, can implement various functions, even complicated ones. The experimental results also show that our DAG model can run in acceptable time.
License type:
PublisherCopyrights
Funding Info:
Core funding
Description:
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1558-2191
1041-4347
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