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Privacy Preserving Big Data Publishing: Challenges, Techniques, and Architectures

Privacy Preserving Big Data Publishing: Challenges, Techniques, and Architectures

Nancy Victor, Daphne Lopez
Copyright: © 2021 |Pages: 18
ISBN13: 9781799889540|ISBN10: 1799889548|EISBN13: 9781799889557
DOI: 10.4018/978-1-7998-8954-0.ch060
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MLA

Victor, Nancy, and Daphne Lopez. "Privacy Preserving Big Data Publishing: Challenges, Techniques, and Architectures." Research Anthology on Privatizing and Securing Data, edited by Information Resources Management Association, IGI Global, 2021, pp. 1281-1298. https://doi.org/10.4018/978-1-7998-8954-0.ch060

APA

Victor, N. & Lopez, D. (2021). Privacy Preserving Big Data Publishing: Challenges, Techniques, and Architectures. In I. Management Association (Ed.), Research Anthology on Privatizing and Securing Data (pp. 1281-1298). IGI Global. https://doi.org/10.4018/978-1-7998-8954-0.ch060

Chicago

Victor, Nancy, and Daphne Lopez. "Privacy Preserving Big Data Publishing: Challenges, Techniques, and Architectures." In Research Anthology on Privatizing and Securing Data, edited by Information Resources Management Association, 1281-1298. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8954-0.ch060

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Abstract

Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.

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