Privacy-preserving decision trees over vertically partitioned data-Reference-Cited by-同舟云学术

Privacy-preserving decision trees over vertically partitioned data

Published:2008-10 Issue:3 Volume:2 Page:1-27
ISSN:1556-4681
Container-title:ACM Transactions on Knowledge Discovery from Data
language:en
Short-container-title:ACM Trans. Knowl. Discov. Data

Author:

Vaidya Jaideep¹,Clifton Chris²,Kantarcioglu Murat³,Patterson A. Scott⁴

Affiliation:

1. Rutgers University, Newark, NJ

2. Purdue University, West Lafayette, IN

3. University of Texas at Dallas, Richardson, TX

4. Johns Hopkins University, Baltimore, MD

Abstract

Privacy and security concerns can prevent sharing of data, derailing data-mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. We introduce a generalized privacy-preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with a proof of security, we discuss what would be necessary to make the protocols completely secure. We also provide experimental results, giving a first demonstration of the practical complexity of secure multiparty computation-based data mining.

Funder

Division of Information and Intelligent Systems

Air Force Office of Scientific Research

Division of Computer and Network Systems

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/1409620.1409624

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