Title: Big data and big risk: a four-factor framework for big data security and privacy

Authors: Yanjun Zuo

Addresses: University of North Dakota, 292 Centennial Drive, Grand Forks, ND 58202, USA

Abstract: Big data refers to a very large volume of data with possibly varied and complex structure. With growing data processing and data analytic techniques, big data provides significant benefits to organisations and individuals by improving productivity and enriching people's life. However, security and privacy are big concerns for big data applications. While a large quantity of data is collected, securely storing, processing and using the data are challenging. In this paper, we propose a four-factor framework for big data security and privacy in business information systems. The proposed framework addresses big data security and privacy issues in terms of collecting the right data, collecting the right amount of data, protecting the data in the right way, and using the data for the right purposes. We present a set of approaches and models for each of the four factors to improve big data security and privacy.

Keywords: big data; security; privacy; model.

DOI: 10.1504/IJBIS.2023.128648

International Journal of Business Information Systems, 2023 Vol.42 No.2, pp.224 - 242

Received: 15 Nov 2019
Accepted: 09 May 2020
Published online: 01 Feb 2023 *

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