Abstract
Despite the importance of big data, it faces many challenges. The most important big data challenges are data storage, heterogeneity, inconsistency, timeliness, security, scalability, visualization, fault tolerance, and privacy. This paper concentrates on privacy which is one of the most pressing issues with big data. As mentioned in the Literature Review below there are numerous methods for safeguarding privacy with big data. This paper introduces an efficient technique called Specialized Negative Database (SNDB) for protecting privacy in big data. SNDB is proposed to avoid the drawbacks of all previous techniques. SNDB is based on deceiving bad users and hackers by replacing only sensitive attribute with its complement. Bad user cannot differentiate between the original data and the data after applying this technique.
Highlights
One of the most pressing challenges in big data is data privacy
We classified likely privacy violations in big data systems into four categories based on a literature review: data breaches, reidentification attacks, information gathering by service providers, and government tracking
5) Perturbation: The actual data values are replaced with generated data values in perturbation, resulting in statistical information acquired from modified data that is statistically similar to that computed from the original data [6]
Summary
One of the most pressing challenges in big data is data privacy. Patients' data must be kept private since there is a risk of improper use of personal information being exposed when data from multiple sources is combined. We classified likely privacy violations in big data systems into four categories based on a literature review: data breaches, reidentification attacks, information gathering by service providers, and government tracking. The motivation of this manuscript is the importance of preserving privacy for everyone specially when dealing with big data. The section will introduce literature review of previous techniques and their drawbacks. While, proposed technique and the manuscript contribution will be introduced. The author will introduce datasets used in proposed technique. Conclusion and future work will be introduced [1], [2], [3], [4]
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