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A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

Anitha J., Prasad S. P.
Copyright: © 2020 |Pages: 17
ISBN13: 9781522596110|ISBN10: 1522596119|ISBN13 Softcover: 9781522596127|EISBN13: 9781522596134
DOI: 10.4018/978-1-5225-9611-0.ch016
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MLA

Anitha J., and Prasad S. P. "A Secure Protocol for High-Dimensional Big Data Providing Data Privacy." Handbook of Research on Machine and Deep Learning Applications for Cyber Security, edited by Padmavathi Ganapathi and D. Shanmugapriya, IGI Global, 2020, pp. 347-363. https://doi.org/10.4018/978-1-5225-9611-0.ch016

APA

Anitha J. & Prasad S. P. (2020). A Secure Protocol for High-Dimensional Big Data Providing Data Privacy. In P. Ganapathi & D. Shanmugapriya (Eds.), Handbook of Research on Machine and Deep Learning Applications for Cyber Security (pp. 347-363). IGI Global. https://doi.org/10.4018/978-1-5225-9611-0.ch016

Chicago

Anitha J., and Prasad S. P. "A Secure Protocol for High-Dimensional Big Data Providing Data Privacy." In Handbook of Research on Machine and Deep Learning Applications for Cyber Security, edited by Padmavathi Ganapathi and D. Shanmugapriya, 347-363. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9611-0.ch016

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Abstract

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.

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