A Survey of Privacy Concerns in Wearable Devices

Most of the countries with elderly populations are currently facing with chronic diseases. In this regard, Internet of Things (IoT) technology offers promising tools for reducing the chronic disease burdens. Despite the presence of fruitful works on the use of IoT for chronic disease management in literature, these are rarely overviewed consistently. The present study provides an overview on the use of IoT for chronic disease management, followed by ranking different chronic diseases based on their priority for using IoT in the developing countries. For this purpose, a structural coding was used to provide a list of technologies adopted so far, and then latent Dirichlet allocation algorithm was applied to find major topics in literature. In order to rank chronic diseases based on their priority for using IoT, a list of common categories of chronic diseases was subjected to fuzzy analytic hierarchy process. The research findings include lists of IoT technologies for chronic disease management and the most-discussed chronic diseases. In addition, with the help of text mining, a total of 18 major topics were extracted from the relevant pieces of literature. The results indicated that the cardiovascular disease and to a slightly lesser extent, diabetes mellitus are of the highest priorities for using IoT in the context of developing countries.

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“…In cases where the main Google search engine and Google Scholar were utilized, some of the labels were inspired by some studies. [ 75 76 77 ]…”

Section: Resultsmentioning

confidence: 99%

Use of Internet of Things for Chronic Disease Management

Dadkhah¹,

Mehraeen²,

Rahimnia³

et al. 2021

Journal of Medical Signals &Amp; Sensors

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“…The proposed UJAmI Location system contains specific data regarding location and daily life, that is, information of a private nature. Security considerations need to be addressed in the mobile device [52]. In our case, the device only collects the RSSI values emitted by the beacons.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the data privacy and security of IPSs, many of them, including the one proposed in this paper, use mobile devices that include built-in sensors to obtain accurate location data so as to inform about physical activity level and mental health. The study provided in [52] showed that data could be easily accessible when using mobile devices, and that users are unaware of the dangers involved and have a false sense of privacy. There is no unified solution to cover all threats in mobile technology security.…”

Section: Indoor Location In the Context Of Ageingmentioning

confidence: 99%

UJAmI Location: A Fuzzy Indoor Location System for the Elderly

Albín-Rodríguez

Robles

López

et al. 2021

IJERPH

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Due to the large number of elderly people with physical and cognitive issues, there is a strong need to provide indoor location systems that help caregivers monitor as many people as possible and with the best quality possible. In this paper, a fuzzy indoor location methodology is proposed in a smart environment based on mobile devices and Bluetooth Low Energy (BLE) beacons where a set of Received Signal Strength Indicators (RSSI) is received by mobile devices worn by the inhabitants. The use of fuzzy logic and a fuzzy linguistic approach is proposed to deal with the imprecise nature of the RSSI values, which are influenced by external factors such as radio waves, causing significant fluctuations. A case study carried out at the Smart Lab of the University of Jaén (UJAmI Smart Lab) is presented to demonstrate the effectiveness of the proposed methodology, where our proposal is compared with a non-fuzzy logic approach, obtaining an accuracy of 91.63%, approximately 10 points higher than the methodology without using fuzzy logic. Finally, our theoretical proposal is accompanied by a description of the UJAmI Location system, which applies the theory to the functionality of locating elderly people in indoor environments.

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“…Last, we review the actions users take to protect their privacy. Prior work in this area is vast, so literature surveys have been compiled to support researchers [19,21,23,39,48,80,89,92,94]. In the following sections, we will refer to the individual studies and not to the surveys.…”

Section: Related Workmentioning

confidence: 99%

“…For the incorrect mental models (54.6%), we distinguish four types of technical misunderstandings: (i) The most common misunderstanding (28.7%) was for participants thinking that their tracker can directly contact the Fitbit servers without passing through their smartphone, by using Bluetooth, WiFi, or satellites (e.g., Figure 7c). 19 (ii) The second most common misunderstanding (19.4%) was related to the participants who thought their trackers receive the processed data directly from the Fitbit servers. (iii) The least common misunderstanding (1.9%) was from the participants who think their fitness data goes from their tracker to their smartphone, from their smartphone to their computer, and then from their computer to the Fitbit servers.…”

Section: Understanding Of the Information Flow In The Fitness-tracker Ecosystemmentioning

confidence: 99%

Are Those Steps Worth Your Privacy?

Velykoivanenko

Niksirat

Zufferey

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Fitness trackers are increasingly popular. The data they collect provides substantial benefits to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker users perceive the utility of the features they provide and the associated privacy-inference risks. We conduct a longitudinal study composed of a four-month period of fitness-tracker use (N = 227), followed by an online survey (N = 227) and interviews (N = 19). We assess the users' knowledge of concrete privacy threats that fitness-tracker users are exposed to (as demonstrated by previous work), possible privacy-preserving actions users can take, and perceptions of utility of the features provided by the fitness trackers. We study the potential for data minimization and the users' mental models of how the fitness tracking ecosystem works. Our findings show that the participants are aware that some types of information might be inferred from the data collected by the fitness trackers. For instance, the participants correctly guessed that sexual activity could be inferred from heart-rate data. However, the participants did not realize that also the non-physiological information could be inferred from the data. Our findings demonstrate a high potential for data minimization, either by processing data locally or by decreasing the temporal granularity of the data sent to the service provider. Furthermore, we identify the participants' lack of understanding and common misconceptions about how the Fitbit ecosystem works.

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A Survey of Privacy Concerns in Wearable Devices

Cited by 34 publications

References 23 publications

Use of Internet of Things for Chronic Disease Management

Use of Internet of Things for Chronic Disease Management

UJAmI Location: A Fuzzy Indoor Location System for the Elderly

Are Those Steps Worth Your Privacy?

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