Privacy Management in Global Organisations

Pearson, Siani

doi:10.1007/978-3-642-32805-3_23

Cited by 10 publications

(5 citation statements)

References 32 publications

(30 reference statements)

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“…Data sharing is a widely acknowledged practice that enhances organisational efficiency and performance by providing insights into processes and technologies. However, it can present challenges, particularly in relation to privacy concerns [12,13]. The nature of data sharing varies among organisations, depending on their business models, such as B2B, B2C, or C2C, which dictate distinct relationship models and privacy policies [14].…”

Section: Challenges In Organisational Data Sharingmentioning

confidence: 99%

An Analytical Review of Industrial Privacy Frameworks and Regulations for Organisational Data Sharing

Ghorashi,

Zia,

Bewong

et al. 2023

Applied Sciences

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This study examines the privacy protection challenges in data sharing between organisations and third-party entities, focusing on changing collaborations in the digital age. Utilising a mixed-method approach, we categorise data-sharing practices into three business models, each with unique privacy concerns. The research reviews legal regulations like the General Data Protection Regulation (GDPR), highlighting their emphasis on user privacy protection but noting a lack of specific technical guidance. In contrast, industrial privacy frameworks such as NIST and Five Safes are explored for their comprehensive procedural and technical guidance, bridging the gap between legal mandates and practical applications. A key component of this study is the analysis of the Facebook–Cambridge Analytica data breach, which illustrates the significant privacy violations and their wider implications. This case study demonstrates how the principles of the NIST and Five Safes frameworks can effectively mitigate privacy risks, enhancing transparency and accountability in data sharing. Our findings highlight the dynamic nature of data sharing and the vital role of both privacy regulations and industry-specific frameworks in protecting individual privacy rights. This study contributes insights into the development of robust privacy strategies, highlighting the necessity of integrating comprehensive privacy frameworks into organisational practices for improved decision making, operational efficiency, and privacy protection in collaborative data environments.

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Section: Challenges In Organisational Data Sharingmentioning

confidence: 99%

An Analytical Review of Industrial Privacy Frameworks and Regulations for Organisational Data Sharing

Ghorashi,

Zia,

Bewong

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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“…Internal tools supporting accountable privacy management are sometimes used by companies to help employees make decisions affecting personal data. HP uses a decision support engine called the Privacy Advisor [32], used for the assessment of privacy risks. Staff are helped to make decisions regarding data handling after filling out a questionnaire, including criteria such as handled information types, the handling of specific types of sensitive information and so on.…”

Section: Related Frameworkmentioning

confidence: 99%

A Guide to End-to-End Privacy Accountability

Butin

Métayer

2015

2015 IEEE/ACM 1st International Workshop on TEchnical and LEgal Aspects of Data pRivacy and SEcurity

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“…We present in this section a synthesis of recent reviews on privacy-enhancing technologies (PETs), namely [30,84,99,104,108]. The concept of PET can be traced back to the early 1980s with the work of Chaum [25], Pfitzmann [87] and others focusing on confidentiality in communications as an application of the recent invention of public key cryp tography.…”

Section: Privacy Technologymentioning

confidence: 99%

A survey on addressing privacy together with quality of context for context management in the Internet of Things

et al. 2013

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International audienceMaking the Internet of Things (IoT) a reality will contribute to extend the context-aware ability of numerous sensitive applications. We can foresee that the context of users will include not only their own spatio-temporal conditions but also those of the things situated in their ambient environment and at the same time, thanks to the IoT, those that are located in other remote spaces. Consequently, next-generation context managers have to interact with the IoT underlying technologies and must, even more than before, address both privacy and quality of context (QoC) requirements. In this article, we show that the notions of privacy and QoC are intimately related and sometimes contradictory and survey the recent works addressing them. Current solutions usually consider only one notion, and very few of them started to bridge privacy and QoC. We identify some of the remaining challenges that next-generation context managers have to deal with to favour users' acceptability by providing both the optimal QoC level and the appropriate privacy protectio

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Privacy Management in Global Organisations

Cited by 10 publications

References 32 publications

An Analytical Review of Industrial Privacy Frameworks and Regulations for Organisational Data Sharing

An Analytical Review of Industrial Privacy Frameworks and Regulations for Organisational Data Sharing

A Guide to End-to-End Privacy Accountability

A survey on addressing privacy together with quality of context for context management in the Internet of Things

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