Getting Smarter about Smart Cities: Improving Data Security and Privacy through Compliance

Abstract

Smart cities assure the masses a higher quality of life through digital interconnectivity, leading to increased efficiency and accessibility in cities. In addition, a huge amount of data is being exchanged through smart devices, networks, cloud infrastructure, big data analysis and Internet of Things (IoT) applications in the various private and public sectors, such as critical infrastructures, financial sectors, healthcare, and Small and Medium Enterprises (SMEs). However, these sectors require maintaining certain security mechanisms to ensure the confidentiality and integrity of personal and critical information. However, unfortunately, organizations fail to maintain their security posture in terms of security mechanisms and controls, which leads to data breach incidents either intentionally or inadvertently due to the vulnerabilities in their information management systems that either malicious insiders or attackers exploit. In this paper, we highlight the importance of data breaches and issues related to information leakage incidents. In particular, the impact of data breaching incidents and the reasons contributing to such incidents affect the citizens’ well-being. In addition, this paper also discusses various preventive measures such as security mechanisms, laws, standards, procedures, and best practices, including follow-up mitigation strategies.

Leveraging Big Data for Sme Growth and Competitiveness: A Literature Review

“…These challenges range from an insufficient understanding and a lack of big data thinking to inadequate investment and an incomplete big data infrastructure. Furthermore, irregular management and the low quality of foundational big data, coupled with an incomplete introduction and a shortage of big data talents, exacerbate these challenges (Aslam et al, 2022).…”

“…While large corporations, especially industry leaders, have long recognized big data's power and have actively deployed in related fields, SMEs often find themselves at a disadvantage. This disparity arises from both objective and subjective constraints, including limitations in big data hardware, technology, and talent reserves (Aslam et al, 2022).…”

“…The proliferation of smart devices, networks, cloud infrastructure, big data analysis, and Internet of Things (IoT) applications has led to an exponential increase in data exchange across various sectors, including critical infrastructures, financial sectors, healthcare, and notably, Small and Medium Enterprises (SMEs). This data, often encompassing sensitive personal and critical information, necessitates robust security mechanisms to ensure its confidentiality and integrity (Aslam et al, 2022).…”

Exploring the Impact of METO Factors with Data Privacy Changes at IoT Systems: A Comprehensive Review

“…Clear privacy requirements [4] facilitate a better understanding of internal policy development, meaning that any change in privacy requirements leads to changes in management, environmental, technological and organizational (METO) factors. Data privacy has become increasingly changed based on numerous high-profile data breaches, potential risks associated with personal misuse activities, regulations of individuals' privacy rights, compliance with privacy laws [5], and change in requirements from business demands [6]. The changes in privacy on the existing system could involve enhancing techniques related to data sharing [6], the expansion of network infrastructures, or cybersecurity frameworks [7] for covering the potential risks.…”

Big Data Analytics in Healthcare: Exploring the Role of Machine Learning in Predicting Patient Outcomes and Improving Healthcare Delivery

“…For example, machine learning models can be used to predict patient demand for healthcare services, identify areas of inefficiency in healthcare delivery, and optimize resource allocation. However, this research implies the use of machine learning in healthcare using a hypothesized model, including the need to ensure the accuracy and reliability of models, protect patient privacy and security (Aslam et al, 2022), and integrate machine learning into clinical workflows. As healthcare continues to generate vast amounts of data, the potential of machine learning to improve patient outcomes and healthcare performance is likely to continue to grow.…”