“Privacy by Design” implementation: Information system engineers’ perspective

Bu, Fei; Wang, Nengmin; Jiang, Bin; Liang, Huigang

doi:10.1016/j.ijinfomgt.2020.102124

Cited by 34 publications

(18 citation statements)

References 88 publications

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“…We are in a new digital age when the response of the society and the privacy of its individuals must be of their own choice, and not a new imposed standard for the management of user information ( Cabalquinto and Hutchins, 2020 ). Concepts such as ‘surveillance capitalism’ ( Zuboff, 2019 ) must not gain strength or cause this new era of surveillance to become an opportunity for mass control, disinformation, and mismanagement of information ( Bu et al., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

158

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Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users’ mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users’ mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.

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Section: Discussionmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

158

View full text Add to dashboard Cite

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“…The discussions about privacy can take a variety of forms, from seminars and lightning talks, specialised channels (e.g. Slack groups or message threads) dedicated to discussing privacy questions and exchanging resources on the topic, to motivational speeches during all-hands company meetings, where management can show their support and recognition of the importance of privacy values and leverage "social infuences" [12].…”

Section: Support the Motivationsmentioning

confidence: 99%

Privacy Champions in Software Teams: Understanding Their Motivations, Strategies, and Challenges

Tahaei

Frik

Vaniea

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Software development teams are responsible for making and implementing software design decisions that directly impact end-user privacy, a challenging task to do well. Privacy Champions-people who strongly care about advocating privacy-play a useful role in supporting privacy-respecting development cultures. To understand their motivations, challenges, and strategies for protecting end-user privacy, we conducted 12 interviews with Privacy Champions in software development teams. We fnd that common barriers to implementing privacy in software design include: negative privacy culture, internal prioritisation tensions, limited tool support, unclear evaluation metrics, and technical complexity. To promote privacy, Privacy Champions regularly use informal discussions, management support, communication among stakeholders, and documentation and guidelines. They perceive code reviews and practical training as more instructive than general privacy awareness and on-boarding training. Our study is a frst step towards understanding how Privacy Champions work to improve their organisation's privacy approaches and improve the privacy of enduser products. CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing; • Security and privacy → Usability in security and privacy; • Social and professional topics → Software management.

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“…1 In the absence of a vaccine or other drugs to treat COVID positive cases, the government is regulating different policies to exercise social distancing through lockdown measures to curb the spread of this disease. However, there is a serious need for technology interventions to help manage, mitigate, and control this problem by developing various novel solutions through analytical modeling using public health data captured in various online repositories [83]- [84]. The daily progression of COVID in different countries is present on different websites.…”

Section: A Datasetmentioning

confidence: 99%

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

Persis

Manimuthu

Sreedharan

2023

IEEE Trans. Eng. Manage.

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COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

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“Privacy by Design” implementation: Information system engineers’ perspective

Cited by 34 publications

References 88 publications

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Privacy Champions in Software Teams: Understanding Their Motivations, Strategies, and Challenges

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

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