Privacy in Doubt: An Empirical Investigation of Canadians' Knowledge of Corporate Data Collection and Usage Practices

Rice, Marshall D.; Bogdanov, Ekaterina

doi:10.1002/cjas.1494

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…A substantial body of research shows that Canadian preferences on data sharing are informed by dubious beliefs about data collection and use policies [110]. In order to reveal true population-level preferences, research must be conducted in a way that clearly outlines data usage policies, ensures understanding and elicits candid responses.…”

Section: Population-level Testing About Sharing Preferencesmentioning

confidence: 99%

COVI White Paper

Alsdurf,

Belliveau,

Bengio

et al. 2020

Preprint

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The SARS-CoV-2 (Covid-19) pandemic has resulted in significant strain on health care and public health institutions around the world. Contact tracing is an essential tool for public health officials and local communities to change the course of the Covid-19 pandemic. Standard manual contact tracing of people infected with Covid-19, while the current gold standard, has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile applications has the potential to shift the paradigm of Covid-19 community spread. Although some countries have deployed centralized tracking systems through either GPS or Bluetooth, more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or in for-profit corporations.Additionally, machine learning methods can be used to circumvent some of the limitations of standard digital tracing by incorporating many clues (including medical conditions, self-reported symptoms, and numerous encounters with people at different risk levels, for different durations and distances) and their uncertainty into a more graded and precise estimation of infection and contagion risk. The estimated risk can be used to provide early risk awareness, personalized recommendations and relevant information to the user and connect them to health services. Finally, the non-identifying data about these risks can inform detailed epidemiological models trained jointly with the machine learning predictor, and these models can provide statistical evidence for the interaction and importance of different factors involved in the transmission of the disease. They can also be used to monitor, evaluate and optimize different health policy and confinement/deconfinement scenarios according to medical and economic productivity indicators.However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of 'COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.

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Section: Population-level Testing About Sharing Preferencesmentioning

confidence: 99%

COVI White Paper

Alsdurf,

Belliveau,

Bengio

et al. 2020

Preprint

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show abstract

“…Realising the rapid changes and developments in IoT and other emerging technologies, this paper provides a flexible and dynamic approach to tackling emerging issues whilst recognising that consumer awareness of IoT privacy and security is crucial to the success of IoT adoption [18,19], whereby concerns of security and privacy can affect consumers' buying, trust, and confidence on consumer IoT devices [20]. Nevertheless, it can be noted that the consumers in developing countries may be less aware of their rights and risks [21].…”

Section: Introductionmentioning

confidence: 99%

Protecting the Unprotected Consumer Data in Internet of Things: Current Scenario of Data Governance in Malaysia

Cheryl

2022

Sustainability

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With the increasing number of smart devices and connections in Internet of Things (IoT) comes risks—specifically involving consumer data protection. In this respect, this exploratory research examines the current issues of IoT and personal data protection in Malaysia that includes: regulatory frameworks and data governance; issues and gaps; and key challenges in implementation. Results from this mixed-methods research indicates that a majority of consumers expressed concern about personal data risks due to increased usage of IoT devices. Moreover, there is a crucial need to increase regulation and accountability in the industry. In this regard, collaboration and partnerships between the main stakeholders are essential in tackling emerging issues of IoT and personal data protection. In order to strengthen IoT data governance, the fundamentals should be: strengthening consumer education and smart partnership between government-industry-civil society; providing motivation for active participation of NGOs and civil society; and obtaining industry buy-in. This paper also proposes a structure for the governance of evolving data-related technology, particularly in the case of data breaches or cyber incidents. It adds to the wider discussion of the current scenario, and proposes a model of collective responsibility in IoT data governance that is underpinned by the three principles of fair information practices, privacy impact assessment and privacy accountability.

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“…China's internet finance industry has strong negative externality, which has been another focus of spillover effect and risk management targeting the field of internet finance (Guo & Shen, 2016). Fourthly, big data are widely used in information related to internet finance (Rice & Bogdanov, 2019; Xie et al., 2016), which could engender a lot of potential risks. Risk control is key to internet finance (Huang et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

How to establish a coordinated supervisory mechanism of internet finance companies in China?

et al. 2021

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This paper aims to explore how local government can be involved in regulating the digital innovations of internet finance companies in China. Game models were used to study the behaviors between internet finance companies and local government, and between local government and central regulatory agencies. The following phenomena were observed: in a static game, internet finance companies will tend to choose high-risk innovation activities, while local government can effectively prevent the high risks inherent to innovation by participating in supervision. In a dynamic game, internet finance companies will take rent-seeking approaches to evade punishment, which lower the likelihood of local government supervision. From these results we can propose policy implications for regulating digital innovation of internet finance companies.

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Privacy in Doubt: An Empirical Investigation of Canadians' Knowledge of Corporate Data Collection and Usage Practices

Cited by 10 publications

References 29 publications

COVI White Paper

COVI White Paper

Protecting the Unprotected Consumer Data in Internet of Things: Current Scenario of Data Governance in Malaysia

How to establish a coordinated supervisory mechanism of internet finance companies in China?

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