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Title: Enhanced privacy preservation using hybridization of Cuckoo search and SVM
Authors: Sharma S
Ahuja S.
Keywords: Artificial Bee Colony (ABC)K-anonymityPrivacy preservationSupport Vector Machine (SVM)
Issue Date: 2020
Publisher: Science and Engineering Research Support Society
Abstract: Rising popularity of internet has also raised the instances of online attacks compromising user sensitive data. K-anonymity popularly offers a very simple and practical approach to deal with privacy protection in social networks. In this paper, authors concentrated on the improvement of k-anonymity to offer enhanced privacy protection from the network anomalies. The proposed enhanced privacy protection design involves hybridization of Artificial Bee Colony (ABC) and machine learning architecture of Support Vector Machine (SVM). The study outcomes of the hybrid approach are evaluated for ARNET and SDFB datasets against the existing privacy preservation designs of Namdarzadegan,Khafaei and Macwan et al. in terms of APL and Song et al. for information loss. Overall, the proposed hybridization exhibited 1.72% and 1.46% lower information loss with 1.42% to 5.09% reduced APL for ARNET and SDFB datasets in comparison to the existing work. � 2019 SERSC.
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