Privacy-preserving frequent itemsets mining via secure collaborative framework

Kok Seng Wong, Myung Ho Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Knowledge-discovering or pattern-discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi-party computation (SMC) approach has been widely used to solve privacy-preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy-preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions. Furthermore, our solution is more reliable and flexible regardless of the number of parties involved.

Original languageEnglish
Pages (from-to)263-272
Number of pages10
JournalSecurity and Communication Networks
Volume5
Issue number3
DOIs
Publication statusPublished - Jan 1 2012
Externally publishedYes

Fingerprint

Data mining
Data privacy

Keywords

  • Collaborative framework
  • Distributed association rules mining
  • Frequent itemsets mining
  • Multi-party computation
  • Privacy preserving

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

Cite this

Privacy-preserving frequent itemsets mining via secure collaborative framework. / Wong, Kok Seng; Kim, Myung Ho.

In: Security and Communication Networks, Vol. 5, No. 3, 01.01.2012, p. 263-272.

Research output: Contribution to journalArticle

@article{0bb9609a3aef4148a7184f326ddcf217,
title = "Privacy-preserving frequent itemsets mining via secure collaborative framework",
abstract = "Knowledge-discovering or pattern-discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi-party computation (SMC) approach has been widely used to solve privacy-preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy-preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions. Furthermore, our solution is more reliable and flexible regardless of the number of parties involved.",
keywords = "Collaborative framework, Distributed association rules mining, Frequent itemsets mining, Multi-party computation, Privacy preserving",
author = "Wong, {Kok Seng} and Kim, {Myung Ho}",
year = "2012",
month = "1",
day = "1",
doi = "10.1002/sec.335",
language = "English",
volume = "5",
pages = "263--272",
journal = "Security and Communication Networks",
issn = "1939-0114",
publisher = "John Wiley and Sons Inc.",
number = "3",

}

TY - JOUR

T1 - Privacy-preserving frequent itemsets mining via secure collaborative framework

AU - Wong, Kok Seng

AU - Kim, Myung Ho

PY - 2012/1/1

Y1 - 2012/1/1

N2 - Knowledge-discovering or pattern-discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi-party computation (SMC) approach has been widely used to solve privacy-preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy-preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions. Furthermore, our solution is more reliable and flexible regardless of the number of parties involved.

AB - Knowledge-discovering or pattern-discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi-party computation (SMC) approach has been widely used to solve privacy-preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy-preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions. Furthermore, our solution is more reliable and flexible regardless of the number of parties involved.

KW - Collaborative framework

KW - Distributed association rules mining

KW - Frequent itemsets mining

KW - Multi-party computation

KW - Privacy preserving

UR - http://www.scopus.com/inward/record.url?scp=84863159517&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863159517&partnerID=8YFLogxK

U2 - 10.1002/sec.335

DO - 10.1002/sec.335

M3 - Article

VL - 5

SP - 263

EP - 272

JO - Security and Communication Networks

JF - Security and Communication Networks

SN - 1939-0114

IS - 3

ER -