Abstract
The raw data dissemination and publication are very important elements in applications like commercial, academic, medical, etc. Due to the rapid increase in large amount of open technical platforms like social networks, IOT devices, electronics gazettes, and mobile devices, the volume of data has grown over time. The very large data sets that are collected from these platforms have any size and structure. Such characteristics of data add more challenges in extracting results or applying further processing by analyzing and storing. To extract the meaningful knowledge or hidden pattern, big data analytics is used from this massive scale complex data. In both unstructured and structured data, the sensitivity does not be captured by the big data traditional model. Security of big data plays an important role due to the speed of ingesting and processing of data in big data analytics. A small change in data may lead to many complexities to user data. The privacy and security of big data analytics contradict with the extensive use of big data. There is a need of stronger encryption in order to secure user data. Thus, it is a time of need to improve security at several levels for privacy preservation of data. This chapter is concerned with security and privacy in big data. The aim is to encrypt data by developing multiple level of encrypting techniques. The private or personal data may be contained in big data which should not be leaked out. Knowledge mining in big data keeping the privacy measures is a major concern of data. A strong encryption model in form of multilevel is proposed for privacy preservation to the data in big data analytics. For wide applications, cryptography technique plays an important role in providing privacy to sensitive attributes in big data.
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Jena, L., Mohanty, R., Mohanty, M.N. (2021). Privacy-Preserving Cryptographic Model for Big Data Analytics. In: Das, P.K., Tripathy, H.K., Mohd Yusof, S.A. (eds) Privacy and Security Issues in Big Data. Services and Business Process Reengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-1007-3_7
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