A Survey of Privacy Concerns in Wearable Devices

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.

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Privacy Concerns of Overweight Adults to use Wearable Devices for Sustained Health Monitoring

10.29007/mpfc ◽

2019 ◽

Author(s):

Oluwaseyi Ogundele ◽

Liezel Cilliers

Keyword(s):

Health Monitoring ◽

Sampling Technique ◽

Wearable Devices ◽

Behavioural Intention ◽

Third Party ◽

Research Approach ◽

Privacy Concerns ◽

Overweight Adult ◽

Overweight Adults

The market for wearable devices that can be used for sustained health monitoring purposes is continuously growing within the healthcare sec- tor. However, to function effectively, these devices must collect a large amount of data from the users. There are privacy concerns that may inhibit the behavioural intention of overweight adult to use wearable de- vices for health monitoring in the long term. This study examined the privacy factors influencing the behavioural intention of overweight adult to make use of wearable devices of sustained health monitoring. The study made use of a qualitative research approach with an inter- view design. A purposive sampling technique was used to select and interview twenty overweight adults (aged 18-59 years) who are using wearable devices in East London, South Africa. The Expectation Confirmation Model (ECM) framework was adopted as the underlying re- search theory in this study. Thematic analysis was used to analyse the data provided by participants. The results found that there were 4 levels of privacy concerns among users. Some users were very concerned that their data was collected by the device manufacturing, while others had not concern at all. Some users had privacy concerns, but did not think that the data collected would be useful to a third party and finally some users did have privacy concerns, but indicated that the benefit of using a wearable device outweighed their concerns and they would continue to use the device. The recommendation of the study is that users must educate themselves about what data is collected and how it will be used by third parties.

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Current Advances in Wearable Devices and Their Sensors in Patients With Depression

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.672347 ◽

2021 ◽

Vol 12 ◽

Author(s):

Seunggyu Lee ◽

Hyewon Kim ◽

Mi Jin Park ◽

Hong Jin Jeon

Keyword(s):

Real Time ◽

Wearable Devices ◽

Literature Survey ◽

Diagnosis And Treatment ◽

Future Trends ◽

Data Reliability ◽

Privacy Concerns ◽

Objective Monitoring ◽

Novel Approaches ◽

Fitness Trackers

In this study, a literature survey was conducted of research into the development and use of wearable devices and sensors in patients with depression. We collected 18 studies that had investigated wearable devices for assessment, monitoring, or prediction of depression. In this report, we examine the sensors of the various types of wearable devices (e.g., actigraphy units, wristbands, fitness trackers, and smartwatches) and parameters measured through sensors in people with depression. In addition, we discuss future trends, referring to research in other areas employing wearable devices, and suggest the challenges of using wearable devices in the field of depression. Real-time objective monitoring of symptoms and novel approaches for diagnosis and treatment using wearable devices will lead to changes in management of patients with depression. During the process, it is necessary to overcome several issues, including limited types of collected data, reliability, user adherence, and privacy concerns.

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Wearable Devices and Privacy Concerns

Privacy Concerns Surrounding Personal Information Sharing on Health and Fitness Mobile Apps - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-3487-8.ch004 ◽

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.

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Effective Privacy-Preserving Collection of Health Data from a User’s Wearable Device

Applied Sciences ◽

10.3390/app10186396 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6396

Author(s):

Jong Wook Kim ◽

Su-Mee Moon ◽

Sang-ug Kang ◽

Beakcheol Jang

Keyword(s):

Differential Privacy ◽

Service Providers ◽

Healthcare Services ◽

Wearable Devices ◽

Privacy Preserving ◽

Health Data ◽

Experimental Results ◽

Sensitive Information ◽

Privacy Concerns ◽

Primary Means

The popularity of wearable devices equipped with a variety of sensors that can measure users’ health status and monitor their lifestyle has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data from users. Although the health data collected via wearable devices are useful for providing healthcare services, the indiscriminate collection of an individual’s health data raises serious privacy concerns. This is because the health data measured and monitored by wearable devices contain sensitive information related to the wearer’s personal health and lifestyle. Therefore, we propose a method to aggregate health data obtained from users’ wearable devices in a privacy-preserving manner. The proposed method leverages local differential privacy, which is a de facto standard for privacy-preserving data processing and aggregation, to collect sensitive health data. In particular, to mitigate the error incurred by the perturbation mechanism of location differential privacy, the proposed scheme first samples a small number of salient data that best represents the original health data, after which the scheme collects the sampled salient data instead of the entire set of health data. Our experimental results show that the proposed sampling-based collection scheme achieves significant improvement in the estimated accuracy when compared with straightforward solutions. Furthermore, the experimental results verify that an effective tradeoff between the level of privacy protection and the accuracy of aggregate statistics can be achieved with the proposed approach.

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A Clustering K-Anonymity Privacy-Preserving Method for Wearable IoT Devices

Security and Communication Networks ◽

10.1155/2018/4945152 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 8

Author(s):

Fang Liu ◽

Tong Li

Keyword(s):

Internet Of Things ◽

Wearable Devices ◽

Third Party ◽

The Internet ◽

Specific Data ◽

Privacy Concerns ◽

The Third ◽

Massive Scale ◽

Iot Devices ◽

The Internet Of Things

Wearable technology is one of the greatest applications of the Internet of Things. The popularity of wearable devices has led to a massive scale of personal (user-specific) data. Generally, data holders (manufacturers) of wearable devices are willing to share these data with others to get benefits. However, significant privacy concerns would arise when sharing the data with the third party in an improper manner. In this paper, we first propose a specific threat model about the data sharing process of wearable devices’ data. Then we propose a K-anonymity method based on clustering to preserve privacy of wearable IoT devices’ data and guarantee the usability of the collected data. Experiment results demonstrate the effectiveness of the proposed method.

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