Privacy preserving association rule mining

Y. Saygin, V. S. Verykios, Ahmed Elmagarmid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

102 Citations (Scopus)

Abstract

The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data, impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to that of the original algorithms.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Workshop on Research Issues in Data Engineering
PublisherIEEE Computer Society
Pages151-158
Number of pages8
Volume2002-January
ISBN (Print)0769514804
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems, RIDE-2EC 2002 - San Jose, United States
Duration: 24 Feb 200225 Feb 2002

Other

Other12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems, RIDE-2EC 2002
CountryUnited States
CitySan Jose
Period24/2/0225/2/02

Fingerprint

Association rules
Data mining
Space applications
Security of data

Keywords

  • Association rules
  • Data engineering
  • Data mining
  • Data privacy
  • Data security
  • Educational institutions
  • Hardware
  • Information security
  • Internet
  • Space technology

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Saygin, Y., Verykios, V. S., & Elmagarmid, A. (2002). Privacy preserving association rule mining. In Proceedings of the IEEE International Workshop on Research Issues in Data Engineering (Vol. 2002-January, pp. 151-158). [995109] IEEE Computer Society. https://doi.org/10.1109/RIDE.2002.995109

Privacy preserving association rule mining. / Saygin, Y.; Verykios, V. S.; Elmagarmid, Ahmed.

Proceedings of the IEEE International Workshop on Research Issues in Data Engineering. Vol. 2002-January IEEE Computer Society, 2002. p. 151-158 995109.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Saygin, Y, Verykios, VS & Elmagarmid, A 2002, Privacy preserving association rule mining. in Proceedings of the IEEE International Workshop on Research Issues in Data Engineering. vol. 2002-January, 995109, IEEE Computer Society, pp. 151-158, 12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems, RIDE-2EC 2002, San Jose, United States, 24/2/02. https://doi.org/10.1109/RIDE.2002.995109
Saygin Y, Verykios VS, Elmagarmid A. Privacy preserving association rule mining. In Proceedings of the IEEE International Workshop on Research Issues in Data Engineering. Vol. 2002-January. IEEE Computer Society. 2002. p. 151-158. 995109 https://doi.org/10.1109/RIDE.2002.995109
Saygin, Y. ; Verykios, V. S. ; Elmagarmid, Ahmed. / Privacy preserving association rule mining. Proceedings of the IEEE International Workshop on Research Issues in Data Engineering. Vol. 2002-January IEEE Computer Society, 2002. pp. 151-158
@inproceedings{c9649eb8bf194fc79fb0beb0754cbe9e,
title = "Privacy preserving association rule mining",
abstract = "The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data, impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to that of the original algorithms.",
keywords = "Association rules, Data engineering, Data mining, Data privacy, Data security, Educational institutions, Hardware, Information security, Internet, Space technology",
author = "Y. Saygin and Verykios, {V. S.} and Ahmed Elmagarmid",
year = "2002",
doi = "10.1109/RIDE.2002.995109",
language = "English",
isbn = "0769514804",
volume = "2002-January",
pages = "151--158",
booktitle = "Proceedings of the IEEE International Workshop on Research Issues in Data Engineering",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Privacy preserving association rule mining

AU - Saygin, Y.

AU - Verykios, V. S.

AU - Elmagarmid, Ahmed

PY - 2002

Y1 - 2002

N2 - The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data, impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to that of the original algorithms.

AB - The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data, impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to that of the original algorithms.

KW - Association rules

KW - Data engineering

KW - Data mining

KW - Data privacy

KW - Data security

KW - Educational institutions

KW - Hardware

KW - Information security

KW - Internet

KW - Space technology

UR - http://www.scopus.com/inward/record.url?scp=33746083334&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33746083334&partnerID=8YFLogxK

U2 - 10.1109/RIDE.2002.995109

DO - 10.1109/RIDE.2002.995109

M3 - Conference contribution

AN - SCOPUS:33746083334

SN - 0769514804

VL - 2002-January

SP - 151

EP - 158

BT - Proceedings of the IEEE International Workshop on Research Issues in Data Engineering

PB - IEEE Computer Society

ER -