Wearable Devices and Privacy Concerns

2021 ◽  
pp. 83-111
Author(s):  
Ersin Dincelli ◽  
Xin Zhou ◽  
Alper Yayla ◽  
Haadi Jafarian
Keyword(s):  
Data Collection ◽  
Social Networking ◽  
Data Analytics ◽  
Data Privacy ◽  
Personal Data ◽  
Wearable Devices ◽  
Physical Activities ◽  
Lifestyle Habits ◽  
Privacy Concerns ◽  
Daily Physical Activities

Wearable devices have evolved over the years and shown significant increase in popularity. With the advances in sensor technologies, data collection capabilities, and data analytics, wearable devices now enable interaction among users, devices, and their environment seamlessly. Multifunctional nature of this technology enables users to track their daily physical activities, engage with other users through social networking capabilities, and log their lifestyle habits. In this chapter, the authors discuss the types of sensor technologies embedded in wearable devices and how the data collected through such devices can be further interpreted by data analytics. In parallel with abundance of personal data that can be collected via wearable devices, they also discuss issues related to data privacy, suggestions for users, developers, and policymakers regarding how to protect data privacy are also discussed.

Wearable Devices and Privacy Concerns

2021 ◽  
pp. 208-230
Author(s):  
Ersin Dincelli ◽  
Xin Zhou ◽  
Alper Yayla ◽  
Haadi Jafarian
Keyword(s):  
Data Collection ◽  
Social Networking ◽  
Data Analytics ◽  
Data Privacy ◽  
Personal Data ◽  
Wearable Devices ◽  
Physical Activities ◽  
Lifestyle Habits ◽  
Privacy Concerns ◽  
Daily Physical Activities

Wearable devices have evolved over the years and shown significant increase in popularity. With the advances in sensor technologies, data collection capabilities, and data analytics, wearable devices now enable interaction among users, devices, and their environment seamlessly. Multifunctional nature of this technology enables users to track their daily physical activities, engage with other users through social networking capabilities, and log their lifestyle habits. In this chapter, the authors discuss the types of sensor technologies embedded in wearable devices and how the data collected through such devices can be further interpreted by data analytics. In parallel with abundance of personal data that can be collected via wearable devices, they also discuss issues related to data privacy, suggestions for users, developers, and policymakers regarding how to protect data privacy are also discussed.

Employing a Mixed Method to Explore Mobile Social Media Users' Big Data Privacy Concerns

2015 ◽  
pp. 302-320
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Data Privacy ◽  
Mobile Technologies ◽  
Personal Privacy ◽  
Privacy Concerns ◽  
Early 21St Century ◽  
Data Practices ◽  
Networking Technologies

Since its introduction in the early 21st century, mobile social media have played an indispensable part in contemporary human experiences. The convergence of social networking and mobile technologies and services creates a fascinating circumstance because the pervasive nature of mobile social networking technologies has impacted on users' privacy. The chapter employed a mixed research method to collect and analyze mobile social media users' experiences and privacy concerns in the age of Big Data. A total of 57 participants were included in this study. Collected data was analyzed by examining mobile social media users' experiences and their concerns over privacy. Findings from this study showed the rising concerns over personal privacy as a result of convergence of mobile social media and Big Data practices by the advertising industry. Theoretical and practical implications were discussed.

scholarly journals That’s Private! Understanding Travelers’ Privacy Concerns and Online Data Disclosure

2020 ◽  
pp. 004728752095164
Author(s):  
Athina Ioannou ◽  
Iis Tussyadiah ◽  
Graham Miller
Keyword(s):  
Data Privacy ◽  
Personal Information ◽  
Empirical Studies ◽  
Personal Data ◽  
Tourism Industry ◽  
Online Data ◽  
Biometric Data ◽  
Identity Verification ◽  
Privacy Concerns ◽  
Travel And Tourism

Against the backdrop of advancements in technology and its deployment by companies and governments to collect sensitive personal information, information privacy has become an issue of great interest for academics, practitioners, and the general public. The travel and tourism industry has been pioneering the collection and use of biometric data for identity verification. Yet, privacy research focusing on the travel context is scarce. This study developed a valid measurement of Travelers’ Online Privacy Concerns (TOPC) through a series of empirical studies: pilot ( n=277) and cross-validation ( n=287). TOPC was then assessed for its predictive validity in its relationships with trust, risk, and intention to disclose four types of personal data: biometric, identifiers, biographic, and behavioral data ( n=685). Results highlight the role of trust in mitigating the relationship between travelers’ privacy concerns and data disclosure. This study provides valuable contribution to research and practice on data privacy in travel.

scholarly journals Ok Google: Using virtual assistants for data collection in psychological and behavioral research

2021 ◽  
Author(s):  
Philipp Sprengholz ◽  
Cornelia Betsch
Keyword(s):  
Data Collection ◽  
Data Privacy ◽  
Reliability And Validity ◽  
Behavioral Research ◽  
Social Psychological ◽  
Online Questionnaire ◽  
Privacy Concerns ◽  
Wide Availability ◽  
Lower Education ◽  
The Many

AbstractBecause of the increasing popularity of voice-controlled virtual assistants, such as Amazon’s Alexa and Google Assistant, they should be considered a new medium for psychological and behavioral research. We developed Survey Mate, an extension of Google Assistant, and conducted two studies to analyze the reliability and validity of data collected through this medium. In the first study, we assessed validated procrastination and shyness scales as well as social desirability indicators for both the virtual assistant and an online questionnaire. The results revealed comparable internal consistency and construct and criterion validity. In the second study, five social psychological experiments, which have been successfully replicated by the Many Labs projects, were successfully reproduced using a virtual assistant for data collection. Comparable effects were observed for users of both smartphones and smart speakers. Our findings point to the applicability of virtual assistants in data collection independent of the device used. While we identify some limitations, including data privacy concerns and a tendency toward more socially desirable responses, we found that virtual assistants could allow the recruitment of participants who are hard to reach with established data collection techniques, such as people with visual impairment, dyslexia, or lower education. This new medium could also be suitable for recruiting samples from non-Western countries because of its wide availability and easily adaptable language settings. It could also support an increase in the generalizability of theories in the future.

scholarly journals Trust and The Acquisition and Use of Public Health Information

2021 ◽  
Author(s):  
Stephen Holland ◽  
Jamie Cawthra ◽  
Tamara Schloemer ◽  
Peter Schröder-Bäck
Keyword(s):  
Public Health ◽  
Health Information ◽  
Data Privacy ◽  
Empirical Work ◽  
Personal Data ◽  
Health Data ◽  
Privacy Concerns ◽  
External Threats ◽  
Trust In Institutions ◽  
The Way

AbstractInformation is clearly vital to public health, but the acquisition and use of public health data elicit serious privacy concerns. One strategy for navigating this dilemma is to build 'trust' in institutions responsible for health information, thereby reducing privacy concerns and increasing willingness to contribute personal data. This strategy, as currently presented in public health literature, has serious shortcomings. But it can be augmented by appealing to the philosophical analysis of the concept of trust. Philosophers distinguish trust and trustworthiness from cognate attitudes, such as confident reliance. Central to this is value congruence: trust is grounded in the perception of shared values. So, the way to build trust in institutions responsible for health data is for those institutions to develop and display values shared by the public. We defend this approach from objections, such as that trust is an interpersonal attitude inappropriate to the way people relate to organisations. The paper then moves on to the practical application of our strategy. Trust and trustworthiness can reduce privacy concerns and increase willingness to share health data, notably, in the context of internal and external threats to data privacy. We end by appealing for the sort of empirical work our proposal requires.

Secure Building Blocks for Data Privacy

2011 ◽  
pp. 1741-1746
Author(s):  
Shuguo Han
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Data Storage ◽  
Data Privacy ◽  
Building Blocks ◽  
Modern World ◽  
Data Sets ◽  
Privacy Concerns ◽  
Data Mining Algorithms ◽  
Wide Availability

Rapid advances in automated data collection tools and data storage technology have led to the wide availability of huge amount of data. Data mining can extract useful and interesting rules or knowledge for decision making from large amount of data. In the modern world of business competition, collaboration between industries or companies is one form of alliance to maintain overall competitiveness. Two industries or companies may find that it is beneficial to collaborate in order to discover more useful and interesting patterns, rules or knowledge from their joint data collection, which they would not be able to derive otherwise. Due to privacy concerns, it is impossible for each party to share its own private data with one another if the data mining algorithms are not secure. Therefore, privacy-preserving data mining (PPDM) was proposed to resolve the data privacy concerns while yielding the utility of distributed data sets (Agrawal & Srikant, 2000; Lindell.Y. & Pinkas, 2000). Conventional PPDM makes use of Secure Multi-party Computation (Yao, 1986) or randomization techniques to allow the participating parties to preserve their data privacy during the mining process. It has been widely acknowledged that algorithms based on secure multi-party computation are able to achieve complete accuracy, albeit at the expense of efficiency.

Employing a Mixed Method to Explore Mobile Social Media Users' Big Data Privacy Concerns

2016 ◽  
pp. 1528-1548
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Data Privacy ◽  
Mobile Technologies ◽  
Personal Privacy ◽  
Privacy Concerns ◽  
Early 21St Century ◽  
Data Practices ◽  
Networking Technologies

Since its introduction in the early 21st century, mobile social media have played an indispensable part in contemporary human experiences. The convergence of social networking and mobile technologies and services creates a fascinating circumstance because the pervasive nature of mobile social networking technologies has impacted on users' privacy. The chapter employed a mixed research method to collect and analyze mobile social media users' experiences and privacy concerns in the age of Big Data. A total of 57 participants were included in this study. Collected data was analyzed by examining mobile social media users' experiences and their concerns over privacy. Findings from this study showed the rising concerns over personal privacy as a result of convergence of mobile social media and Big Data practices by the advertising industry. Theoretical and practical implications were discussed.

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