combining top down and bottom up approach for data anonymization using map reduce on cloud -- 2

Κλειστό Αναρτήθηκε Jun 8, 2015 Πληρώθηκε κατά την παράδοση
Κλειστό Πληρώθηκε κατά την παράδοση

In big data applications, data privacy is one of the

most concerned issues because processing large-scale

privacy-sensitive data sets often requires computation power

provided by public cloud services. Sub-tree data

anonymization, achieving a good trade-off between data

utility and distortion, is a widely adopted scheme to

anonymize data sets for privacy preservation. Top-Down

Specialization (TDS) and Bottom-Up Generalization (BUG)

are two ways to fulfill sub-tree anonymization. However,

existing approaches for sub-tree anonymization fall short of

parallelization capability, thereby lacking scalability in

handling big data on cloud. Still, both TDS and BUG suffer

from poor performance for certain value of k-anonymity

parameter if they are utilized individually. In this paper, we

propose a hybrid approach that combines TDS and BUG

together for efficient sub-tree anonymization over big data.

Further, we design MapReduce based algorithms for two

components (TDS and BUG) to gain high scalability by

exploiting powerful computation capability of cloud.

Experiment evaluations demonstrate that the hybrid

approach significantly improves the scalability and

efficiency of sub-tree anonymization scheme over existing

approaches.

Cloud Computing Hadoop Java

Ταυτότητα Εργασίας: #7820228

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4 προτάσεις Απομακρυσμένη εργασία Ενεργό Jul 15, 2015

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