Privacy-Preserving Sequential Pattern Mining Over Vertically Partitioned Data

Authors

Justin Zhan, School of Information Technology & Engineering, University of Ottawa, CanadaFollow
Stan Matwin, School of Information Technology & Engineering, University of Ottawa, CanadaFollow
LiWu Chang, Center For High Assurance Computer Systems, Naval Research Laboratory, USAFollow

Document Type

Article

Abstract

Privacy-preserving data mining in distributed environments is an important issue in the field of data mining. In this paper, we study how to conduct sequential patterns mining, which is one of the data mining computations, on private data in the following scenario: Multiple parties, each having a private data set, want to jointly conduct sequential pattern mining. Since no party wants to disclose its private data to other parties, a secure method needs to be provided to make such a computation feasible. We develop a practical solution to the above problem in this paper.

Recommended Citation

Zhan, Justin; Matwin, Stan; and Chang, LiWu, "Privacy-Preserving Sequential Pattern Mining Over Vertically Partitioned Data" (2005). ICEB 2005 Proceedings (Hong Kong, SAR China). 83.
https://aisel.aisnet.org/iceb2005/83