NDLI: Data security rules/regulations based classification of file data using TsF-kNN algorithm

Content Provider	Springer Nature Link
Author	Zardari, Munwar Ali Jung, Low Tang
Copyright Year	2016
Abstract	Personal and organizational data are getting larger in volume with respect to time. Due to the importance of data for organisations, effective and efficient management and categorization of data need a special focus. Understanding and applying data security policies to the appropriate data types therefore is one of the core concerns in large organisations such as cloud service providers. With data classification, the identification of security requirements for the data can be accomplished without manual intervention where the encryption process is applied only to the confidential data thus saving encryption time, decryption time, storage and processing power. The proposed data classification approach is to reduce the network traffic, the additional data movement, the overload, and the storage place for confidential data can be decided where security requirements of the confidential data are fulfilled. In this paper, an intelligent data classification approach is presented for predicting the confidentiality/sensitivity level of the data in a file based on the corporate objective and government policies/rules. An enhanced version of the k-NN algorithm is also proposed to reduce the computational complexity of the traditional k-NN algorithm at data classification phase. The proposed algorithm is called Training dataset Filtration-kNN (TsF-kNN). The experimental results show that data in a file can be classified into confidential and non-confidential classes and TsF-kNN algorithm has better performance against the traditional k-NN and Naïve Bayes algorithm.
Starting Page	349
Ending Page	368
Page Count	20
File Format	PDF
ISSN	13867857
Journal	Cluster Computing
Volume Number	19
Issue Number	1
e-ISSN	15737543
Language	English
Publisher	Springer US
Publisher Date	2016-02-02
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data classification k-NN Data security File attributes Encryption Data security policies Bi-gram TsF-kNN Processor Architectures Operating Systems Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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