Accession Number:

ADA464602

Title:

Privacy-Preserving Collaborative Data Mining

Descriptive Note:

Corporate Author:

NAVAL RESEARCH LAB WASHINGTON DC CENTER FOR HIGH ASSURANCE COMPUTING SYSTEMS (CHACS)

Personal Author(s):

Report Date:

2003-01-01

Pagination or Media Count:

9.0

Abstract:

Privacy-preserving data mining is an important issue in the areas of data mining and security. In this paper, we study how to conduct association rule mining, one of the core data mining techniques, on private data in the following scenario Multiple parties, each having a private data set, want to jointly conduct association rule mining without disclosing their private data to other parties. Because of the interactive nature among parties, developing a secure framework to achieve such a computation is both challenging and desirable. In this paper, we present a secure framework for multiple parties to conduct privacy-preserving association rule mining.

Subject Categories:

  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE