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Embracing the Digital Era: The Influence of Computers on Modern Society

Background Studies

The computer age characterised extensive use of computers and computerised devices and extending to the development of supporting structures coupled by influence on aspects of humankind living. Computers have developed to being core part, if not influencing every aspects, of lifestyle ranging from sporting world, social, entertainment to business. Survey conducted by Chen et al. (2012) and Schiavi and Behr (2018), highlighted that international businesses and large corporations largely depend on computerised systems in conducting their respective operations particularly connectivity, data collection, and effectiveness of workforces and systems. However, according to Inglehart (2018), cultural shift in business operations to technology based are not limited to big and well-established organisations but beneficial across the board no matter the financial support, market share, or success rate. In essence, the number of people connected and using internet accessing digital media has surged considerably over the past decade. A survey by United Nation on digital connection, mobile phone usage, and social media users found that 67% (5.112 billion people), 57% (4.388 billion people), and 45% (3.484 billion people) of the world population are mobile phone, internet, and social media users respectively.

Digital connection globally, January 2019

Compared to January 2018, the users in the three categories increased by 2%, 9.1%, and 9.0% respectively. Similarly, in the emerging technology perspective, such innovative tools and ecosystems as augmented reality, virtual reality, artificial intelligence, Blockchain, sensing and mobility, Internet of Things, and complimented by Cloud Computing have rapidly grown becoming prominent in several fields. Collectively, the major concerns connecting all these emerging technologies are factors related to users’ privacy and security. Connecting computerised devices, data-generating, buildings, and assets founded on the collection, processing, and analysing data advanced location, machine-to-machine communication, and machine learning leading to predictive and prescriptive analytics. In essence, these interconnection and virtual storage means more data

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Problem statement and justification

To some extent, the argument of consumers (users) having fundamental data-privacy rights and protection have taken centre stage in emerging technology paradigm. The increasing development and penetration of emerging technologies ranging from AI, deep learning, Internet of Things (IoT), to Cloud computing demand supporting infrastructures that include data centres and data storage facilities (Demchenko et al., 2013; Wang et al., 2017; ). In some countries, for instance, in European, the governments and companies are required to have a legal basis and to protect any private data collected that include having correct security measures in place. Additionally, individual are reserved with such privilege of deciding what and ways in which personal is to be stored. Currently, more and more data in being generated and supporting structures that includes storage and transfer devices being faster developed becoming harder and harder to keep up with.

Although most governments authorities and institutions globally have tried to set in place legal framework completing organizations and institutions to provide adequate rules and policy designed to protect data, evolving technologies evident by huge data generated and emerging technologies has proven challenge in putting put security and privacy infrastructure. The data misuse and breach scandals such as the Cambridge Analytica and Snowden revelations revealed the extent to which the personal data and privacy can be violated big data and AI. Cambridge Analytica allegedly collected private information from Facebook profiles that include users’ friends contacts and details then using the data to influence and wage a ‘cultural wars’ resulting in modelling people behaviour evident in the United States 2016 general election. Control of who access individual’s person information as well as protection of personal sphere of life has been threaten by advancing technology. The rise cloud computing, IoT, deep learning, and big data as well as big corporations, which can access, store, and process Exabyte’s of data has exacerbate the issues of privacy and data security. Interconnecting devices through ‘internet of things’ umbrella allowing sending and receiving data over a network as well as development ‘virtual storage’ platform under ‘cloud computing’ pose a problems of ways in which data security and users privacy is upheld.

In the US, such regulatory statutes California Consumer Privacy Act (CCPA) and Mind Your Own Business Act were formulated aimed at data privacy and outlining the penalties accompanying violation. Implemented in 2018, the General Data Protection Regulation (GDPR) outlining the privacy and security measures within EU on data collection, storage, and usage by companies and governmental institutions have given rise to wide perspective data measures. As pointed by Conger et al. (2013), a major concern in advancing technologies is putting together supporting parameters. Data gathering and interpretation goes beyond personal data and privacy but rather touching national and international security making it an essential factor in contemporary society.

Nevertheless, constant evolution in the technology worlds encompassing rapid emergence of new innovation and rapid changes surrounding current technologies have exerted pressure on existing security and privacy. As argued by Dorri et al. (2017) and Zhou et al. (2017), there is ‘fear of unknown’ on security and data privacy around the future technologies but not limited to emerging technology with unlimited applications. Continuing development and adoption of these emerging technologies open up to numerous vulnerability particularly at the infrastructural part.

Research questions

1. What are the measures set in place to protect private data from being misused or accessed by unauthorised individuals?

2. What are the structures development to secure data collection, storage, and usage in the wake of computing infrastructure advancement?

3. What are the infrastructural challenges faced by emerging computing infrastructure in protecting data and privacy?

4. Are there infrastructural challenges in developing security and privacy measures in IoT ecosystem?

5. What are the security and privacy challenges encountered in cloud computing ecosystem, as emerging computing infrastructure?

Aim of the research

Fast development in new technologies has given rise to concern of privacy and potential misuse of personal information and protection of personal data. Given that data breach and misuse by big corporations such social-ranking system, tracking consumers, and striving security system have become rampant and concerning issues for any technology. As such, this research aims to investigate the security and privacy challenges of emerging computing infrastructures focusing particularly on Internet of Things (IoT) and cloud computing

Research objectives

To understand the security and privacy challenges, the following objectives will be the basis

1. To critically review literature on privacy and security-related issues of information technology

2. To analysis critically policies, countermeasures, and challenges of security and privacy of technology

3. To investigate the challenges related to security and privacy of emerging computing infrastructure

4. To investigate the challenges faced in developing secure and privacy-oriented IoT and cloud computing infrastructure

5. To analysis collected primary and secondary data then formulate recommendation on ways mitigating the security and privacy challenges faced in IoT and cloud computing ecosystem

Methodology

Research philosophy

Building from the arguments by Hammersley (1993) and Hughes and Sharrock (2016), research is developing logical inferences based on data systematically collected and interpreted. However, development of these logical reasoning is supported by contemporary ideas establishing line of thoughts through structural stages. Guba and Lincoln (1982) contended that importance of research philosophical framework is rooted on the belief system that gives a researcher’s perspective and, such, reflection of immediate world guiding the investigation. According to Saunders et al. (2009) and Holden & Lynch (2004), knowledge development and understanding is built on assumptions based on the one’s perspective of the world. As such, in this case, computing infrastructure has grown into core aspects of contemporary societies affecting such fields ranging from businesses, communication, and social lives. Businesses depend on data collected from consumers such as preferences, market size, consumption behaviour, to supply chain while, on the other hand, governing authorities might perceive controlling and monitoring individual citizens boils down to knowing precisely people’s details, behaviour, movement, and thinking. Collectively, with the rise of computing technologies such as AI, IoT, and machine learning, organizations as well as governing bodies and persons with individualistic gains can access and misuse such data.

Essentially, challenges facing implementation of security and privacy measures particularly on the emerging computing infrastructure can both observable social reality and variable social actors. Arguably, personal data and concept of privacy can widely vary from society-to-society. As pointed by Solove (2008) and Matzner (2014), parameters considered private in one society may completely opposite in another, perceived public. Therefore, in line with investigating security and privacy challenges in computing infrastructure, positivism philosophical paradigm will be used. Taylor and Medina (2011) described positivism ideological reasoning as an approach of developing knowledge through natural phenomena and their properties resulting in capturing ‘factual’ information. According to Anyan (2013), an inference on the research problem is based on data collection and interpretation in an objective manner.

In contrast, challenges in implementation of security and privacy measures can be attributed to social factors. Arguably, adoption of any technological measures that include protection infrastructure is subject to social perception of security and privacy. For instance, there are reports of several governments such as China is reportedly collecting and using personal data to track and monitor terror activities, although, this should not be misinterpreted as consensual but can be authoritarian approach. Societies might be inclined side with tracking using social media and other digital media in detection of terror or deterrent of misinformation and propaganda (Patrikarakos, 2017; Levinson-Waldman, 2018; Ward, 2017). As such, this give rise to a fine line between privacy and protection of personal data and larger social perception of good and bad. Therefore, in attempt to understanding such factors informing security and privacy issues in implementation of computing infrastructure, this research will take interpretivist approach. According to Johari (2009), interpretivism holding that every data reality is subject to individual viewpoint and interpretation in accordance to the ideological positions held. Building assertion that knowledge is personally experience rather than imposed or acquired from outside elements, understanding challenges of privacy and security faced by adoption of IoT and cloud computing demands taking multifaceted reality approach and is subject to multiple interpretation.

Therefore, this research will takes both the factual perspectives as well social reality giving it a holistic approach based on either single or multiple empirical inquiry. According to Goldkuhl (2012), the pragmatism contented that opinion held by individuals are objective reality rooted on one experience but such reality is subject to environment and social interaction informing individual experience (Tashakkori et al., 1998; Goles, and Hirschheim, 2000). Yefimov (2004) illustrated that knowledge and reality are built on habits and beliefs are socially constructed but it also acknowledges that social constructions varies with individuals’ experiences. As such, security and privacy will be taken as normative concept and maintaining the reality of the same as what works.

Research approach and design

In conducting a research, a systematic plan outlines the process and procedure to adequately answer and address extensively the goals and objectives. Sekaran and Bougie (2016) outlined that the research approach encompasses steps that include assumption taken into account before and during data collection, processing and analysis, and interpretation of acquired data. Using the logic that the fourth industrial revolution is a product of information technology and deep learning setting data as a centre for business successes and government monitoring and control of its people. Similarly, although structures and legislation have been put in place to protection and uphold privacy of the users, and ensuring data remains secure. This research in security and privacy challenges of emerging computing infrastructure will adopt a mixed approach. As described by Gelo et al. (2008) and Venkatesh et al. (2013), the quantitative part of the approach will encompass quantifiable data and statistical analysed information. Whereas, the qualitative approach will give a platform providing an insight into security and privacy related computing infrastructure and challenges in addressing the issues encountered. As pointed before, given that privacy as well as security in computing ecosystem tasks a multifaceted aspects and, arguably, rooted on both social reality and factual dimension, understanding challenges of faced in development and implementation of security and privacy of such technologies as IoT and cloud computing. Notably, security and privacy infrastructure of such technologies as social media exists but given that emerging computing technologies is not yet well understood that include full potential not explored same approach cannot be taken. As such, using mixed research approach will aid in qualifying and understanding existing measures.

Data collection method

Harrell and Bradley (2009) described data collection as process of collecting information used to answer and address the raised research problem and objectives. However, according to Gill et al. (2008), the process incorporates a systematic approach in order to use relevant sources and asking relevant questions aligned the research aim. In this case, addressing security and privacy challenges of emerging computing infrastructures while being objective demands a considering collecting data either directly from the sources or using existing data in the form of secondary data. In addressing the concept of privacy and security in the scope of emerging computing technologies, this research will used primary data sources. The data collection will be divided into two. First, will involve filling of a questionnaire by identified participant. The findings from the first data collection section will be used to draw themes and set precedent of the second data acquisition process. The participants of the first phrase will identified through online platforms that include LinkedIn, Google+, and social media. The identification process will involve sending an invitation to research participation as well as covering all necessary information (the scope and usefulness of the research). The semi-structured questionnaire will be sent to potential participants who responded to the invitations with little screening process. Using thematic analysis approach, the findings will be analysis to formulate themes, which form basis of the second phase of the research.

In the second phase of data collection, the selection of participants will largely be based on the themes from first phase. Those identified will be interviewed by asking semi-structured questions modelled from first phase. Depending on the themes identified, the participants will range from 3-5 per theme but participants will not be limited to addressing 1 theme. Data collected from the second phase (interview) will be analysed using thematic analysis tool. The use of online platform to identify the participant is based on idea of having a holistic perspective. However, a key inclusion criteria for in the second phase for data collection is extensive knowledge in security and privacy of computing infrastructure and IoT and cloud computing as emerging technologies.

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References

Anyan, F., 2013. The Influence of Power Shifts in Data Collection and Analysis Stages: A Focus on Qualitative Research Interview. Qualitative Report, 18, p.36.

Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly, pp.1165-1188.

Conger, S., Pratt, J.H. and Loch, K.D., 2013. Personal information privacy and emerging technologies. Information Systems Journal, 23(5), pp.401-417.

Demchenko, Y., Grosso, P., De Laat, C. and Membrey, P., 2013, May. Addressing big data issues in scientific data infrastructure. In 2013 International Conference on Collaboration Technologies and Systems (CTS) (pp. 48-55). IEEE.

Dorri, A., Steger, M., Kanhere, S.S. and Jurdak, R., 2017. Blockchain: A distributed solution to automotive security and privacy. IEEE Communications Magazine, 55(12), pp.119-125.

Etro, F., 2009. The economic impact of cloud computing on business creation, employment and output in Europe. Review of Business and Economics, 54(2), pp.179-208.

Gelo, O., Braakmann, D. and Benetka, G., 2008. Quantitative and qualitative research: Beyond the debate. Integrative psychological and behavioral science, 42(3), pp.266-290.

Gill, P., Stewart, K., Treasure, E. and Chadwick, B., 2008. Methods of data collection in qualitative research: interviews and focus groups. British dental journal, 204(6), pp.291-295.

Goldkuhl, G., 2012. Pragmatism vs interpretivism in qualitative information systems research. European journal of information systems, 21(2), pp.135-146.

Goles, T. and Hirschheim, R., 2000. The paradigm is dead, the paradigm is dead… long live the paradigm: the legacy of Burrell and Morgan. Omega, 28(3), pp.249-268.

Hammersley, M. ed., 1993. Social research: philosophy, politics and practice. Sage.

Harrell, M.C. and Bradley, M.A., 2009. Data collection methods. Semi-structured interviews and focus groups. Rand National Defense Research Inst santa monica ca.

Hughes, J.A. and Sharrock, W.W., 2016. The philosophy of social research. Routledge.

Inglehart, R., 2018. Culture shift in advanced industrial society. Princeton University Press.

Johari, J., 2009. Interpretivism in Information System (IS) Research. Integration & Dissemination, 4.

Matzner, T., 2014. Why privacy is not enough privacy in the context of “ubiquitous computing” and “big data”. Journal of Information, Communication and Ethics in Society.

Schiavi, G.S. and Behr, A., 2018. Emerging technologies and new business models: a review on disruptive business models. Innovation & Management Review.

Sekaran, U. and Bougie, R., 2016. Research methods for business: A skill building approach. John Wiley & Sons.

Solove, D.J., 2008. Understanding privacy.

Tashakkori, A., Teddlie, C. and Teddlie, C.B., 1998. Mixed methodology: Combining qualitative and quantitative approaches (Vol. 46). Sage.

Taylor, P.C. and Medina, M., 2011. Educational research paradigms: From positivism to pluralism. College Research Journal, 1(1), pp.1-16.

Venkatesh, V., Brown, S.A. and Bala, H., 2013. Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS quarterly, pp.21-54.

Wang, K., Yu, J., Yu, Y., Qian, Y., Zeng, D., Guo, S., Xiang, Y. and Wu, J., 2017. A survey on energy internet: Architecture, approach, and emerging technologies. IEEE Systems Journal, 12(3), pp.2403-2416.

Zhou, J., Cao, Z., Dong, X. and Vasilakos, A.V., 2017. Security and privacy for cloud-based IoT: Challenges. IEEE Communications Magazine, 55(1), pp.26-33.


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