Security and Privacy Challenges in Big Data Environment

Security and Privacy Challenges in Big Data Environment

M. Manikandakumar, E. Ramanujam
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-4100-4.ch017
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In this information era, big data is revolutionizing business. The data are generated by each and every user from servers, terminals, smart phones, appliances, satellites, and a range of other sensors on vehicles: military, agriculture, and the like. Anything the end users does online can be traced, stored, and analyzed. It is also possible to analyze from various diverse sources such as social media postings, credit card or e-cash purchases, internet searches, mobile phone locations, etc. Users are willing to provide their private information, linked to their real-life identities, in exchange for faster or better digital services. But, the companies yet may not have the fundamental rights of the user from a security perspective. More risks are associated with big data security. The main purpose of this chapter is to explore the security concerns and privacy issues in big data environments.
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Almost all industries largely implementing the concept of Big Data to store peta bytes data for analyzing, marketing and research based on users perspective to earn superior insights concerning the customers and their business. Since many industries noticed that, storing confidential or private information of the end users in cloud storage (Manikandakumar et al., 2015) or in the dedicated server, those companies have the sole responsiveness for protecting private data from both a legal and a commercial perspective. As with all the new technologies, security and protection seems to be addendum at best. As the definition of Big Data (Gandomi & Haider, 2015), the breaches are also too large, with the possibility of high severe reputational hurt and legal consequence than these recent times.

The new types of data in the organizations that need to analyze the following.

  • Web streams such as e-commerce, weblogs and social network analysis data.

  • Business specific transaction data like Tele Caller records, Geo-spatial data, Temporal and Customer Relational Transaction data.

  • Data generated from various types of sensor such as motion, weather, energy, wind, humidity, vibration, airflow, liquid flow, pressure and health-oriented sensors.

  • Textual data from the large volume of archived documents, external sources or customer interaction data, e-mails, messages, and tweets.

According to a study by the Economist Intelligence Unit (Unit, 2007)

  • Nearly 90% of Industrial experts and business leaders believe that the data is now the raw and fundamental source of production to business such as land, labor, and capital.

  • Using Big data in businesses improves the performance by 26% and it is predicted that it may grow up to 41% in next few years.

  • 58% of business sectors say that they are planning to increase the investment in big data over the next three years.

According to Gartner (Beyer, 2011)

  • About 42% of Information Technology experts stated that, they had invested in big data technology or were planning to do so within a year.

  • Organizations those integrate large volume, various information types and sources into a rational information management communications will outperform their industry peers by more than 20%.

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