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AuthorHamzaoui, Amel
AuthorMalluhi, Qutaibah
AuthorClifton, Chris
AuthorRiley, Ryan
Available date2024-07-17T07:14:51Z
Publication Date2015
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-3-319-17016-9_23
ISSN3029743
URIhttp://hdl.handle.net/10576/56776
AbstractAnonymization methods are an important tool to protect privacy. The goal is to release data while preventing individuals from being identified. Most approaches generalize data, reducing the level of detail so that many individuals appear the same. An alternate class of methods, including anatomy, fragmentation, and slicing, preserves detail by generalizing only the link between identifying and sensitive data. We investigate learning association rules on such a database. Association rule mining on a generalized database is challenging, as specific values are replaced with generalizations, eliminating interesting fine-grained correlations. We instead learn association rules from a fragmented database, preserving fine-grained values. Only rules involving both identifying and sensitive information are affected; we demonstrate the efficacy of learning in such environment.
SponsorThis publication was made possible by NPRP grant #09-256-1-046 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherSpringer
SubjectAnonymity
Association rule mining
Data privacy
Database
Fragmentation
TitleAssociation rule mining on fragmented database
TypeConference Paper
Pagination335-342
Volume Number8872
dc.accessType Abstract Only


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