Enterprise security and privacy: Why adding IoT and big data makes it so much more difficult

Duncan, Bob; Whittington, Mark; Chang, Victor

doi:10.1109/icengtechnol.2017.8308189

Cited by 15 publications

(4 citation statements)

References 31 publications

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“…The use of multiple data sources that may themselves be insecure leads to potentially insecure solutions (Duncan et al, 2017). During the completion of this study, Simon Weckart tricked Google Maps into reporting that 100 cell phones in a wagon were a tra c jam demonstrating the ease with which some big data systems can be fooled by injection of IoT devices (Torres, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…This subtheme also aligns well with the subthemes of minimizing data noise and minimizing data sources. This is because many big data sources are often insecure which leads to potentially inaccurate analysis (Duncan, Whittington, & Chang, 2017). Developing processes for validating data sources is a key governance practice for data scientists.…”

Section: Theme 1: Ensuring Business Centricitymentioning

confidence: 99%

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Exploring Mid-Market Strategies for Big Data Governance

Knapton

2020

Preprint

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Many data scientists are struggling to adopt effective data governance practices as they transition from traditional data analysis to big data analytics. Data governance of big data requires new strategies to deal with the volume, variety, and velocity attributes of big data. The purpose of this qualitative multiple case study was to explore big data governance strategies employed by data scientists to provide a holistic perspective of those data for making decisions. The participants were 10 data scientists employed in multiple mid-market companies in the greater Salt Lake City, Utah area who have strategies to govern big data. This study’s data collection included semi-structured in-depth individual interviews (n = 10) and analysis of process documentation relating to big data governance in those organizations (n = 4). Through thematic analysis, 4 major themes emerged from the study: ensuring business centricity, striving for simplicity, establishing data source protocols, and designing for security. The strategies outlined in this study can lead to positive social change by proactively addressing the ethical use of personally identifiable information in big data. By implementing strategies relating to the segregation of duties, encryption of data, and personal information, data scientists can mitigate contemporary concerns relating to the use of private information in big data analytics.

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Section: Discussionmentioning

confidence: 99%

Section: Theme 1: Ensuring Business Centricitymentioning

confidence: 99%

Exploring Mid-Market Strategies for Big Data Governance

Knapton

2020

Preprint

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show abstract

“…This subtheme also aligns well with the subthemes of minimizing data noise and minimizing data sources. This alignment is because many big data sources are often insecure, leading to potentially inaccurate analysis [60]. Developing processes for validating data sources is a key governance practice for data scientists.…”

Section: Establishing Validation Processesmentioning

confidence: 99%

Exploring Mid-Market Strategies for Big Data Governance

Knapton¹

2023

AAIML

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Many data scientists struggle to adopt effective data governance practices as they transition from traditional data analysis to big data analytics. This qualitative multiple case study explored big data governance strategies used by data scientists employed in 3 mid-market companies in the greater Salt Lake City, Utah area who have strategies to govern big data. Data were collected via 10 semi-structured, in-depth, individual interviews and analysis of 4 organizational process documents. Four major themes emerged from the study: ensuring business centricity, striving for simplicity, establishing data source protocols, and designing for security. One key recommendation from the findings for data scientists is to minimize the data noise typically associated with big data. Implementing these strategies can help data scientists transition from traditional to big data analytics, which could help those organizations be more profitable by gaining competitive advantages. By implementing strategies relating to the segregation of duties, encryption of data, and personal information, data scientists can mitigate contemporary concerns relating to using private information in big data analytics.

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“…Should the data from these sensors be reliant upon an actuator, a wrong decision or operation could then be performed. Hence, security becomes a challenge [42].…”

Section: Self-configured Security Within Iot Ecosystemmentioning

confidence: 99%

Securing Self-organizing IoT Ecosystem: A Distributed Ledger Technology Approach

Joseph

Raffety

Morrow

et al. 2019

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

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The proliferation of the Internet of Things has seen it adopted to practically all aspects of life. There has been an increase in demand for more IoT devices which are manufactured by several companies. This has however left need to address vulnerabilities within and threats to these devices. In many cases, these vulnerabilities arise from manufacturer focus on functionality rather than security. Secure by design IoT devices are rare in the market today. Efforts to address this are being made by the IoT research community, however, more effort is required. Deficiencies of current efforts include accountability of devices and privacy of data generated across the IoT landscape. The aim of this Ph.D. research is to improve the security, privacy, veracity, and trust. The approach developed in this study will be based on non-repudiation of actions among self-organized IoT devices in an IoT Ecosystem by leveraging Distributed Ledger Technology (DLT). A proposed system architecture which relies on the Distributed Ledger Technology and its related features will enable services to be applied to the IoT landscape to achieve aspects of end to end IoT security. The initial progress to date is presented within this manuscript.

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Enterprise security and privacy: Why adding IoT and big data makes it so much more difficult

Cited by 15 publications

References 31 publications

Exploring Mid-Market Strategies for Big Data Governance

Exploring Mid-Market Strategies for Big Data Governance

Exploring Mid-Market Strategies for Big Data Governance

Securing Self-organizing IoT Ecosystem: A Distributed Ledger Technology Approach

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