Disclosure limitation of sensitive rules

M. Atallah, E. Bertino, Ahmed Elmagarmid, M. Ibrahim, V. Verykios

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

249 Citations (Scopus)

Abstract

Data products (macrodata or tabular data and micro-data or raw data records), are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Solutions to this problem require combining several techniques and mechanisms. Recent advances in data mining and machine learning algorithms have, however, increased the security risks one may incur when releasing data for mining from outside parties. Issues related to data mining and security have been recognized and investigated only recently. This paper deals with the problem of limiting disclosure of sensitive rules. In particular it is attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other non-sensitive frequent itemsets. Frequent itemsets are sets of items that appear in the database frequently enough and identifying them is usually the first step toward association/correlation rule or sequential pattern mining. Experimental results are presented along with some theoretical issues related to this problem.

Original languageEnglish
Title of host publicationProceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages45-52
Number of pages8
ISBN (Electronic)0769504531, 9780769504537
DOIs
Publication statusPublished - 1 Jan 1999
Externally publishedYes
Event1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999 - Chicago, United States
Duration: 7 Nov 1999 → …

Other

Other1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999
CountryUnited States
CityChicago
Period7/11/99 → …

Fingerprint

Data mining
Security of data
Learning algorithms
Learning systems
Disclosure
Data base
Industry
Micro data
Pattern mining
Data security
Sequential patterns
Machine learning
Learning algorithm
Government
Business policy
Public information
Public policy
Business research

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Cite this

Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., & Verykios, V. (1999). Disclosure limitation of sensitive rules. In Proceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999 (pp. 45-52). [836532] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/KDEX.1999.836532

Disclosure limitation of sensitive rules. / Atallah, M.; Bertino, E.; Elmagarmid, Ahmed; Ibrahim, M.; Verykios, V.

Proceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999. Institute of Electrical and Electronics Engineers Inc., 1999. p. 45-52 836532.

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

Atallah, M, Bertino, E, Elmagarmid, A, Ibrahim, M & Verykios, V 1999, Disclosure limitation of sensitive rules. in Proceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999., 836532, Institute of Electrical and Electronics Engineers Inc., pp. 45-52, 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999, Chicago, United States, 7/11/99. https://doi.org/10.1109/KDEX.1999.836532
Atallah M, Bertino E, Elmagarmid A, Ibrahim M, Verykios V. Disclosure limitation of sensitive rules. In Proceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999. Institute of Electrical and Electronics Engineers Inc. 1999. p. 45-52. 836532 https://doi.org/10.1109/KDEX.1999.836532
Atallah, M. ; Bertino, E. ; Elmagarmid, Ahmed ; Ibrahim, M. ; Verykios, V. / Disclosure limitation of sensitive rules. Proceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999. Institute of Electrical and Electronics Engineers Inc., 1999. pp. 45-52
@inproceedings{8e719a80a665440db16b7c6f32e80bfa,
title = "Disclosure limitation of sensitive rules",
abstract = "Data products (macrodata or tabular data and micro-data or raw data records), are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Solutions to this problem require combining several techniques and mechanisms. Recent advances in data mining and machine learning algorithms have, however, increased the security risks one may incur when releasing data for mining from outside parties. Issues related to data mining and security have been recognized and investigated only recently. This paper deals with the problem of limiting disclosure of sensitive rules. In particular it is attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other non-sensitive frequent itemsets. Frequent itemsets are sets of items that appear in the database frequently enough and identifying them is usually the first step toward association/correlation rule or sequential pattern mining. Experimental results are presented along with some theoretical issues related to this problem.",
author = "M. Atallah and E. Bertino and Ahmed Elmagarmid and M. Ibrahim and V. Verykios",
year = "1999",
month = "1",
day = "1",
doi = "10.1109/KDEX.1999.836532",
language = "English",
pages = "45--52",
booktitle = "Proceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Disclosure limitation of sensitive rules

AU - Atallah, M.

AU - Bertino, E.

AU - Elmagarmid, Ahmed

AU - Ibrahim, M.

AU - Verykios, V.

PY - 1999/1/1

Y1 - 1999/1/1

N2 - Data products (macrodata or tabular data and micro-data or raw data records), are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Solutions to this problem require combining several techniques and mechanisms. Recent advances in data mining and machine learning algorithms have, however, increased the security risks one may incur when releasing data for mining from outside parties. Issues related to data mining and security have been recognized and investigated only recently. This paper deals with the problem of limiting disclosure of sensitive rules. In particular it is attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other non-sensitive frequent itemsets. Frequent itemsets are sets of items that appear in the database frequently enough and identifying them is usually the first step toward association/correlation rule or sequential pattern mining. Experimental results are presented along with some theoretical issues related to this problem.

AB - Data products (macrodata or tabular data and micro-data or raw data records), are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Solutions to this problem require combining several techniques and mechanisms. Recent advances in data mining and machine learning algorithms have, however, increased the security risks one may incur when releasing data for mining from outside parties. Issues related to data mining and security have been recognized and investigated only recently. This paper deals with the problem of limiting disclosure of sensitive rules. In particular it is attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other non-sensitive frequent itemsets. Frequent itemsets are sets of items that appear in the database frequently enough and identifying them is usually the first step toward association/correlation rule or sequential pattern mining. Experimental results are presented along with some theoretical issues related to this problem.

UR - http://www.scopus.com/inward/record.url?scp=85032400475&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032400475&partnerID=8YFLogxK

U2 - 10.1109/KDEX.1999.836532

DO - 10.1109/KDEX.1999.836532

M3 - Conference contribution

AN - SCOPUS:85032400475

SP - 45

EP - 52

BT - Proceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999

PB - Institute of Electrical and Electronics Engineers Inc.

ER -