Enhanced Secured Map Reduce layer for Big Data privacy and security

Abstract

Data security, according to the common definition is confidentiality, integrity, and availability of data. It is the act of guaranteeing that the information is safe from unauthorized access, ensures that the information is reliable and accurate which is accessible whenever it is required. An information security design incorporates features, for example, gathering the required data, protecting it, and obliterating any data that is never again required [1]. Privacy, on the other hand, is the appropriate use of information. In other words, merchants and companies should use the data provided to them only for the intended purpose. For example, if an individual purchase a product from XYZ Company and provides them with their personal information like address, card number, etc. then this company cannot sell that information to the third party. Companies need to enact a data security policy for the sole purpose of ensuring data privacy of their consumer personal information. Moreover, companies must ensure data privacy because the information is an asset to the company. However, no data security policy can overcome the

Preference-Based Privacy Markets

“…Regulation here can curb the adverse effects of these negative externalities arising from trading and significantly contribute to welfare efficient and complete markets (where supply equals demand) [25] [26]. Examples of practical ways to implement regulations suggested in existing literature include legislative property rights on consumer personal data shared between the supply and demand side [14], technical metrics (e.g., DP) being adopted by demand side data intermediaries (e.g., ad-networks) to check on the degree of IP breach [6], and frameworks such as those developed in [27,28,29,30] to improve security and privacy for BigData systems (e.g., HDFS). Specifically, in relation to the data intermediary settings such as in Figure 1, De Corni'ere and Nijs [31] For more details on the interplay between differential privacy and mechanism design, [7] gives a comprehensive survey.…”

Transformation of Technologies to Improve the Efficiency of Business Processes in the Digital Economy

“…For enterprises, the most important aspect of digitalization is to improve the efficiency of business processes. Thus, the most common digitization tool can be considered the use of Big data [17].…”

Influencing subjective well-being for business and sustainable development using big data and predictive regression analysis

“…With organisations reaping much benefit from Big Data Analytics, there is a growing urgency to adopt similar techniques in order to gain real-time insights into individuals’ well-being in order to target aid interventions to vulnerable groups ( UN, 2013 ). While there is a timely discussion pertaining to the ethical use of big data analytics ( Chen & Quan-Haase, 2018 ) with key concerns centred on privacy and security ( Fang et al, 2017 , Jain et al, 2019 ), it is argued that if the abundance of data, advanced technologies, and creative analytical approaches are responsibly enacted, this can lead to responsive, efficient, and evidence-based decision-making which may further improve the progress of the SDG goals, in particular SD3, in a comprehensive and reasonable manner (UN, 2018).…”