عرض بسيط للتسجيلة

المؤلفHamzaoui, Amel
المؤلفMalluhi, Qutaibah
المؤلفClifton, Chris
المؤلفRiley, Ryan
تاريخ الإتاحة2024-07-17T07:14:51Z
تاريخ النشر2015
اسم المنشورLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
المصدرScopus
المعرّفhttp://dx.doi.org/10.1007/978-3-319-17016-9_23
الرقم المعياري الدولي للكتاب3029743
معرّف المصادر الموحدhttp://hdl.handle.net/10576/56776
الملخصAnonymization 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.
راعي المشروعThis 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.
اللغةen
الناشرSpringer
الموضوعAnonymity
Association rule mining
Data privacy
Database
Fragmentation
العنوانAssociation rule mining on fragmented database
النوعConference Paper
الصفحات335-342
رقم المجلد8872
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة