Accession Number:

ADA465063

Title:

Privacy-Preserving Collaborative Sequential Pattern Mining

Descriptive Note:

Corporate Author:

OTTAWA UNIV(ONTARIO) SCHOOL OF INFORMATION TECHNOLOGY

Report Date:

2004-01-01

Pagination or Media Count:

13.0

Abstract:

In the modern business world, collaborative data mining becomes especially important because of the mutual benefit it brings to the collaborators. During the collaboration, each party of the collaboration needs to share its data with other parties. If the parties dont care about their data privacy, the collaboration can be easily achieved. However, if the parties dont want to disclose their private data to each other, can they still achieve the collaboration To use the existing data mining algorithms, all parties need to send their data to a trusted central place to conduct the mining. However in situations with privacy concerns, parties may not trust anyone, including a third party. Generic solutions for any kind of secure collaborative computing exist in the literature. However, none of the proposed generic solutions is practical in handling large-scale data sets because of the prohibitive extra cost in protecting data privacy. Therefore, practical solutions need to be developed. This need underlies the rationale for our research.

Subject Categories:

  • Theoretical Mathematics
  • Computer Systems Management and Standards
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE