Security and Privacy with Big Data Management

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Security and Privacy with Big Data Management

Explore the challenges and solutions associated with ensuring security and privacy in big data management. The essay will discuss the significance of data protection in the era of big data, common threats like breaches and unauthorized access, and the legal and ethical implications. It will offer insights into advanced security protocols, encryption techniques, and compliance with privacy laws. More free essay examples are accessible at PapersOwl about Artificial Intelligence.

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Trying to imagine a world without data is impossible. From every aspect one can think of, data is collected and analyzed and interpreted. Jobs using data are becoming more and more popular as companies, industries, academia and research are looking for people that have skill sets that include mathematics, computer science and data. One major, career or job field that is booming everywhere is in the area of data science. Data scientists use their skillsets learned to interpret data for many different fields including business, healthcare, industry, and sports.

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Currently amounts of data unimaginable not so long ago are being created, collected and stored at rates faster than ever thought. With all this data, there needed to be a way for analysts to be able to access, predict and offer suggestions and solutions in a quick and easy fashion. DSL’s were created to help with this task and often involve cloud computing as well to help with the volume of data ( Zakharchuk, Kovalchuk and Nasonov, 2015).


With the advancement of technologies, large amounts of data are generated and accumulated. According to McKinsey Global Institute on the website article on Big data the next frontier in innovation, “Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” The EdTech Report to the nation 2013 in states “Every day, we create 2.5 quintillion (1020) bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data.” The amount of data humans generate in just 24 hours is equal to 70 times the information held in the Library of Congress (Smolan, R. & Erwitt, J., 2012).

The trend for using technology to make life more convenient is growing at a rapid rate due to the rise of the Internet of Things (IoT) as well also contributing to the collection of big data and the implications of security and privacy. Chandrashekhar et al reported that it is estimated 75 billion devices will be connected by 2020 (Chandrashekhar, A.M et al., 2016). More and more people are getting and staying connected. The millennials are creating and using IoT devices daily with generation Z not far behind. With all these devices and so much data to be collected the creators need to develop safe and secure devices that will keep the consumer and the data safe, secure and private.

Cointe, , Bonnet, and Boissier, 2016), discuss 4 main parts of Business Intelligence which include Systems Source, Acquisition of data, data warehousing and reporting and analysis tools especially for online analytical processing. With gathering the data, companies need to be mindful of the security and privacy of the data collected.

Security and Privacy Issues With Big Data Management

Even though many researchers and practitioners have investigated the initiatives that big data could impact different fields, many scholars raised the concerns of utilizing big data and the impact it has on privacy, security and ethics. In her editorial article, (Eynon, R. 2013) called for attention on big data ethics: “Big Data represents a number of ethical considerations, particularly around privacy, informed consent, and protection of harm, and raises wider questions of what kinds of data should be combined and analyzed, and the purposes to which this should be put” (p.238). Incidents around privacy and publicized data have been discussed among different researchers (Yang, C., Huang, J.-L., & Lin, Z. 2013, Zimmer, M. 2010, Chen, X. & Liu, Y., 2016).

Tene and Polonetsky discuss the importance of the consumers’ rights in opt in and opt out policies where data is collected (Tene, Omar and Jules Polonetsky, 2012). Consumers need to be made aware of where their data goes, what it is used for and how it could be a beneficial tool for marketing and business strategies if choosing to opt in. Many consumers are not aware that they can even choose to opt out of collecting data. With data getting bigger and Wi-Fi being more easily accessible, more and more devices are connected allowing data to be collected and stored. The issue of privacy becomes a big concern for the consumer as the data is growing and the consumer and data are made more vulnerable to threats. As devices are smart, we the consumers have to be SMARTER (Safe, Mature, Aware, Ready, Technologically Educated Responsible).

Asunción (2017) in his article, The Business of Personal Data: Google, Facebook, and Privacy Issues in the EU and the USA, share how Google and Facebook organize their users’ personal data for advertising purposes as well as the privacy issues that arise via privacy policies by the social media giants. Given the latest scandal from Facebook and the Cambridge Analytica data scandal with over 87 million people having their data breached, I predict that the big area of research will be on privacy and security within Social Media data. Tene and Polonetsky (2012) discuss the importance of the consumers rights in opt in and opt out policies where data is collected. Consumers need to be made aware of where their data goes, what it is used for and how it could be a beneficial tool for marketing and business strategies if choosing to opt in. Many consumers are not aware that they can even choose to opt out of collecting data. Gan and Jenkins (2015) reported on a series of experiments that have been undertaken to determine how much information can be obtained about a particular individual using only social networking sites and freely available mining tools. Based on the information gathered by Gan and Jenkins (2015) they found that they could determine a target user of Twitter and then identify such user on other social networking sites such as Foursquare, Instagram, LinkedIn, Facebook and Google+, where more personal information was leaked. Senthil et al. in On Privacy and Security in Social Media – A Comprehensive Study, Procedia Computer Science (2015) state based on their research “It has been observed that privacy concerns are very feeble in the social networking sites and the users endeavors to make the appropriate changes on their social media privacy is substantially lower than other mode of security operations” (p. 119). Spinelli (2010) in his article reports that the social media analytics process adopted from Stieglitz et al. involves four distinct steps, data discovery, collection, preparation, and analysis and that based on his literature search it was determined that the volume of data was most often cited as a challenge by researchers. With so much data and so much vulnerability at stake it is crucial that security is at the forefront and ready for anything that can come its way when managing data.

Existence of and Impact of Security and Privacy with Big Data Management

With increasing use of technology, especially in the area ubiquitous computing and in the area of human interaction involving finances or banking, healthcare or medical issues, children or educational issues, AI is now evolving as a research area that may have issues with privacy, ethical or moral concerns. One must not only consider the advantages that AI may have for the consumer or developer, but must also consider the appropriateness of AI and how the user will be kept safe and his or her usage data remain private. Allen, Smit and Wallach (2006) claim that the ethical issues or burdens one faces when using technology can be shifted to the computer or system itself taking away responsibility from the developer and user. However, the developer will need to ensure that created devices are safe and adhere to security and privacy standards and oblige to not use the data or AI for his or her own means that will hurt the consumer or user. Machines are different from people as the machines can be taught behaviors or knowledge, but will not act on instinct or on their own rationale or moral conscience. There have been many ethical views researched by philosophers on morality ethics, particularly the studies of Kant and Aquinas as noted in Cointe, Bonnet and Boisser (2016). The World Economic Forum notes the following 9 issues in AI: Unemployment, Inequality, Humanity, Artificial Stupidity, Racist robots, Security, Evil genies, Singularity, and Robot rights. If one considers the traditional ethics often philosophized such as virtue ethics, deontological ethics and consequentialist ethics, one can see how each of these 9 mentioned are ethical issues.

Solutions to Reading Data and Making Better Secure Choices

One of the most popular and oldest DSL’s used with relational data is (Structured Query Language or often referred to as SQL. It is considered to be declarative in nature. SQL is primarily used for programming and was created for managing all types of relational data. SQL is often associated with big data and data analysis as it used for queries, table creations and edits along with managing data by altering records. Relational databases on SQL could be platforms for data analytics. Whether it be commercial like IBM, Microsoft SQL Server or Oracle or open source like MySQL or PostgreSQL, relational databases could use this to process the data (Fotache and Strimbei 2015). But using all this data means being extra cautious of how to secure this data.

SQL uses relational algebra as a means to extrapolate the many forms of big data. It has been considered one of the most popular DSL’s because of its ability to be implemented in many different database management systems and its syntax. Because of its popularity SQL was standardized by ISO, ANSI and national agencies with 1986 launching the ANSI standard. SQL queries have many benefits to the user as they can be stored within the database or exported to other types of databases. With the inception of the first SQL standard, all dialects used the basic statistical aggregate functions with common mathematical names like SUM and AVG that do what they say. (Fotache and Strimbei 2015). Additionally, one of the major standards on data analysis for big data was through Online Analytical Processing or OLAP as it is more commonly referred (Fotache and Strimbei 2015).

Although SQL is used in academia, it is more often used in industry or corporations through the fields of data mining and business intelligence or business analytics. Due to the increase in data and the demand for querying SQL had some changes designed as extensions with new operators and functions that could perform statistical analysis. Fotache and Strimbei (2015) report on the findings of Jacobs from 2009 that one of the main issues with SQL relational databases was its inability to properly scale the data. With this came the development of NoSQL which Fotache and Strimbei(2015) found from previous research of Cattell in 2010. It includes horizontal scalability, replication and distribution of data over many servers, simple call level protocols and interfaces, a concurrency model weaker than ACID (atomicity, consistency, isolation, durability), efficient use of RAM for data storage, dynamic capabilities for inserting new attributes into records. SQL could be used to query large corporate databases where management could look at the data to make recommendations and predictions that could better the company and offer a better experience to the consumer. Consumers purchasing specific items could be offered suggestions on what else might make for a good purchase increasing the spending of the customer for a future purchase. This can be done in store or online where the user can be sent suggested items before the cart checkout process. Many companies are using this predict what a consumer may be interested in purchasing. With the predictions being made it is necessary that consumers are made aware of their rights when being offered suggestions or being sent coupons in the mail or email based on prior purchases as others in the household may see or view the coupons suggested from prior purchases.

Prior Research Findings Applied to Security of Big Data and Data Management

Prior research from my information collected shows that Cointe, Bonnet, and Boissier (2016), was useful in determining 4 main things that are part of data management. Guiding principles, policies, strategies, policies as well as other processes and roles were addressed and the framework was designed to implement controls effectively that would ensure successful businesses and business intelligence projects in (Combita ,Cómbita Niñob, and Morales Ortega, 2018). Their research conducted enabled them to design a Business Intelligence governance proposal that could be aligned to the needs and contexts of other institutions that could be used to make decisions that could create a great value for the institution or other type of institution. This is useful for figuring out how to properly manage secure data at any type of organization.

In another useful source Foss and Saebi (2017), discussed their method for utilizing databases in a study. They searched the EBSCO Business Source Premier database for academic articles containing the term “business model innovation” in the abstract and title as well as searched it for keywords to conduct their review of the literature. Having never seen this source, it will be useful for any big data research.

In Schildt, (2017), a discussion on optimizing-oriented systems was shown how eventually it can make employees jobs more meaningful easier and beneficial as it can eventually eliminate many day-to-day jobs that can free up time for employees to focus on different more meaningful job tasks. The use of Natural language processing could provide knowledge management, benefits and solutions within a company, however, utilizing machines, artificial intelligence and machine learning along with NLP can cause other issues within the management structure of the organization that would not happen if it were organized by humans or a combination of human and computer interactions. This is useful and necessary when discussing the issues of privacy and security as human computer interaction is used with business and data management of big data.

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Security and Privacy with Big Data Management. (2021, Oct 16). Retrieved from