Examine How Big Data Analytics Is Affecting Peoples Privacy And Suggest Possible Solutions
Running head: BIG DATA ANALYTICS ON PRIVACY
BIG DATA ANALYTICS ON PRIVACY 7
Big Data Analytics on Privacy
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Big data analytics is a technique employed by much organization especially when dealing with a large number of data to uncover correlation, patterns and any other insights found in the data. For easy and faster analysis of data, technology is used for quick answers. The big data analytics are useful to organizations as it helps them to track any data from the organization for the identification of new opportunities (Tsai et al., 2015). This leads to increased efficiency in organizations operations and also making profits to the company. Although big data analytics have several advantages to the organizations they have been spotted to have affected individual’s privacy in a number of ways such as data breaching, anonymization impossibilities, data masking failures, lack of accurate and unintentional discriminations.
Big data analytics often tend to access ones information and access to the most sensitive data of the person. The organizations are obliged to protect this data from access by authorized persons. However big data analytics may end up breaching someone’s personal information in cases where the data has been poorly secured leading to the hacking of the information (La Torre, Dumay & Rea, 2018). Data breaching often exposes individuals personal information’s to the public which is against the big data ethic that permits the organization as the only to have access to individual’s data. This may cause one to be embarrassed due to the exposure of personal information.
With the high amounts of data in organizations and the improved technology in big data analytics can be very difficult to protect one’s data. These amounts of data may lead to a person’s personal information been re-identified in case of data sets separations. This privacy protection in cases involving anonymization can be achieved through the establishment of laws for anonymization (Karle, & Vora, 2017). The privacy of persons has affected anonymization through failure to enact laws for separating anonymization files that may sometimes lead to exposure of one’s information and may result in the replacement of individual’s data by artificial identifiers.
Failures in data masking have led to the revealing of individuals information. Although data masking is used by any organization to protect sensitive information and ensure intact of data sets it may sometimes be defeated leading to the disclosure of personal information. In organizations where masking is not appropriately done there a chance of being defeated thus exposing individual’s information (Shuan et al., 2016). Through data masking failures the privacy of a person’s information is affected through their information being exposed. In big data analytics, data masking which is a new feature for privacy protection is used by many organization without properly using the techniques required this increases the chances of data breaching hence affects the privacy of persons in a certain organization.
The accurateness of the big data analytics may not be attained. With powerful big data analytics and too many brands used in analytics, the files used in these cases can be inaccurate to give the correct results of the analysis specifically when there might contain flawed algorithm model. This inaccurateness may be as a result of data files been damaged from malicious software or damage by hackers (Yu et al., 2015). The inaccurateness of the results obtained may lead to bad implications about individuals that may lead to them been inappropriately treated.
Big data analytics may result in unintentional discrimination of individuals in organizations. Since the organizations are entitled to access information of every individual in work, possibilities of discrimination of some employees may occur. In cases where the organization needs to promote to employ individuals, the decisions to facilitate those activities generally rely upon big data analytics and thus one may be discriminated basing their race or ethnicity (Reinsch & Goltz, 2016).The algorithms used in big data analytics could, therefore, be used to penalize the candidates of the required positions since the information’s is always “automated” to provide results.
Possible solutions to big data analytics effects to the privacy
The big data analytics has been used to organizations to access their information. Though organizations focus on maintaining the privacy of the individual’s information’s, there are some ways in which privacy has been affected for an instance through data breaching or defeat of data masking among other effects. To prevent such situations the organizations can employ this possible solution to ensure privacy through data governance, compliance, and access management and finally is through anonymization and pseudonymization.
To begin with is the establishment of data governance in organizations. The organizations should establish a structure to govern data by providing directions on how organizations data would be handled and also gives strategies for data protection. Data governance can be achieved by developing organizational structures dealing with data policies and procedures to protect data privacy (Jain, Gyanchandani & Khare, 2016). This would be achieved through defining policies that would outline procedures for data management in data organizations. The data governance in organizations would also help in determining the persons responsible for data protection and thus ensure that not everybody in the organization has access to data. This will increase organization privacy on an individual’s personal information.
The organization needs to understand privacy as a key to the security of their data. Compliance to privacy will allow to understand the processes and used in data storage. Therefore it is important for organizations to establish big data programs that will provide overviews especially in monitoring of data (Colombom & Ferrari, 2015). Compliance can be achieved through the development of control frameworks and establishing ways to prevent risks through implementing strategies to ensure privacy in the organization. Organizations can ensure privacy compliance through making use of automated controls in the transitions process in data protection.
The organizations should effectively control individuals who can access datasets. This can be achieved through the establishment of compressive access management that will involve reviews and approvals of new requests made by any person to access data and evaluations of the previous data users. The organizations can maintain privacy through adopting the separation method in assigning duties especially when access systems are for job functions (Storey & Song, 2017). Access management can be helpful when automated with tools to implements roles, this helps in making decisions on data access. Through access management, privacy is maintained by ensuring that each person’s information is protected from access by unauthorized persons.
Big data analytics privacy effects can also be solved through anonymization and pseudonymization. This involves the removal of personally identifiable information from a particular data sets and changing it to non-identifiable.anonymizations is applicable in long term basis for privacy maintenance of datasets. Privacy can be maintained by anonymization by making sure all removable data is done at data sources, this will help the organization to maintain original datasets privacy. Conversely, the privacy problem could be prevented through pseudonymization that involves replacing identifiable data with non-reversible algorithms in data protection. The algorithms are capable of calculating the encrypted data of individuals thus the information obtained can be combined in one dataset to ensure the privacy of the information (Casabona, 2017). The organizations should take into account of anonymization and pseudonymization techniques to ensure the privacy of the employee’s information’s through avoidance of data being re-identified. These techniques will ensure privacy in the organization since the organization’s data can only be accessed by artificial identifiers only. The risk of privacy through re-identification can be achieved through monitoring of anonymization at every stage of data transmission
In conclusion, big data analytics are very useful in organizations to examine varied data patterns and market trends to identify new opportunities for the organization. Though big data analytics techniques are useful in companies to guide in the decision-making process, have also affected the individual’s privacy. With big data analytics involving access and control of individual’s information privacy has been affected by breaches, data masking failures which have been the cause of revealing personal information to unauthorized individuals. This problems to privacy as a result of big data analytics can be solved by data governance, anonymization and also through data access management to enhance the privacy of information.
References
Casabona, C. M. R. (2017). Anonymization and pseudonymization: the legal framework at a European level. In The Data Protection Directive and Medical Research Across Europe (pp. 33-50). Routledge
Colombo, P., & Ferrari, E. (2015). Privacy aware access control for big data: A research roadmap. Big Data Research, 2(4), 145-154.
Jain, P., Gyanchandani, M., & Khare, N. (2016). Big data privacy: a technological perspective and review. Journal of Big Data, 3(1), 25.
Karle, T., & Vora, D. (2017, February). PRIVACY preservation in big data using anonymization techniques. In 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) (pp. 340-343). IEEE.
La Torre, M., Dumay, J., & Rea, M. A. (2018). Breaching intellectual capital: critical reflections on Big Data security. Meditari Accountancy Research, 26(3), 463-482.
Reinsch, R. W., & Goltz, S. (2016). Big Data: Can the Attempt to Be More Discriminating Be More Discriminatory Instead. . Louis ULJ, 61, 35.
Shuan, L. H., Fei, T. Y., King, S. W., Xiaoning, G., & Mein, L. Z. (2016). Network Equipment Failure Prediction with Big Data Analytics. International Journal of Advances in Soft Computing & Its Applications, 8(3).
Storey, V. C., & Song, I. Y. (2017). Big data technologies and management: What conceptual modeling can do. Data & Knowledge Engineering, 108, 50-67.
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big data, 2(1), 21.
Yu, N., Shah, S., Johnson, R., Sherick, R., Hong, M., & Loparo, K. (2015, February). Big data analytics in power distribution systems. In 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-5). IEEE.
