Protecting E-healthcare Data Privacy for Internet of Things Based Wireless Body Area Network

Rghioui, Anass; L’aarje, Aziza; Elouaai, Fatiha; Bouhorma, Mohammed

doi:10.19026/rjaset.9.2638

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…Some examples of big data collection may seem mundane. Currently, most smartphones are enabled with location sensors, providing real-time data to be collected on an individual's whereabouts and activities (Rghioui, et al, 2015). As more devices are enabled to provide similar information, we observe that cars also provide data on location, while household efficiency and security protection are connected to handheld devices.…”

Section: Big Data and Individual Privacy In The Age Of The Internet Omentioning

confidence: 91%

Big Data and Individual Privacy in the Age of the Internet of Things

Adams¹

2017

Technology Innovation Management Review

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Section: Big Data and Individual Privacy In The Age Of The Internet Omentioning

confidence: 91%

Big Data and Individual Privacy in the Age of the Internet of Things

Adams¹

2017

Technology Innovation Management Review

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“…The creation and use of big data constitute various information privacy-related risks, such as data breach, de-anonymization, and the transferal of information to third parties. Researchers have identified that IoT-based devices that track and analyze extensive user data often lack careful consideration of data security, leading to data breach problems (e.g., Rghioui, Aziza, Elouaai & Bouhorma, 2015). In addition, one of the major challenges in IoT is designing a security system guaranteeing the complete anonymity of collected data to ensure user privacy (e.g., Sanchez, Bernabe & Skarmeta, 2018).…”

Section: Privacy Downsides Of Iotmentioning

confidence: 99%

Private Information Disclosure on the Internet of Things: The Effects of Tailoring, Self-expansion, and Power Usage

Kang

Kim

2020

Journal of Broadcasting & Electronic Media

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Private Information Disclosure on the Internet of Things:The Effects of Tailoring, Self-Expansion, and Power Usage A between-subjects experiment was conducted to explore how users manage their privacy when using the Internet of Things (IoT) providing either user-tailored or system-tailored services. The results indicated that users created multiple privacy boundaries to contain highly private information in the innermost boundary and less sensitive information in the outer boundaries.The results also revealed a significant interaction between the tailoring type and power usage for predicting the degree to which users expanded their selves to IoT. A moderated mediation test showed that power usage and tailoring type interact on disclosure intention of highly sensitive information mediated by self-expansion.

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“…Amplification of these risks to individual privacy are sometimes associated with the Internet of Things (IoT), because the IoT enables digital devices of many kinds to connect with local and virtual networks, communicating automatically with other devices, while continuously generating data (Borgohain, Kuman, & Sanyal, 2015). For example, smartphones are equipped with location-based sensors, generating real-time data about human time-space paths (Rghioui, Aziza, Elouaai, & Bouhorma, 2015). Even in countries where technologies are less accessible, mobile telecommunications services have expanded, as marketers use a plethora of applications that draw on global information systems (GIS) services to inform and enhance the capabilities of the user experience and the functionality of the device (Hayes, 2014, p. 50).…”

Section: Privacy and Dataveillancementioning

confidence: 99%

Big Data in Qualitative Research

2020

SAGE Research Methods Foundations

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Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena.This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers.This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher.

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Protecting E-healthcare Data Privacy for Internet of Things Based Wireless Body Area Network

Cited by 9 publications

References 12 publications

Big Data and Individual Privacy in the Age of the Internet of Things

Big Data and Individual Privacy in the Age of the Internet of Things

Private Information Disclosure on the Internet of Things: The Effects of Tailoring, Self-expansion, and Power Usage

Big Data in Qualitative Research

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