Do You Know Who Is Talking to Your Wearable Smartband?

Kazlouski, Andrei; Marchioro, Thomas; Manifavas, Harry; Markatos, Evangelos P.

doi:10.3233/shti200711

Cited by 2 publications

(7 citation statements)

References 6 publications

(11 reference statements)

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“…(2017) successfully manipulated data on fitness tracking apps by injecting fabricated values in proof-of-concept attacks on wearable device platforms, exemplifying the vulnerability of these apps. Kazlouski et al. (2020) showed that fitness mobile apps contacted unexpected third parties (location services, advertisers and analytic providers), continuing to state that individuals are likely donating private information to device manufacturers in exchange to use the app.…”

Section: Literature Reviewmentioning

confidence: 99%

“…platforms, exemplifying the vulnerability of these apps. Kazlouski et al (2020) showed that fitness mobile apps contacted unexpected third parties (location services, advertisers and analytic providers), continuing to state that individuals are likely donating private information to device manufacturers in exchange to use the app. Grundy et al (2017) validated privacy and security concerns related to prominent health apps by tracing the potential flow of consumers' data through network analysis, in which they identified a core of 15 app families through which data may travel.…”

Section: Integrated Fitness Appsmentioning

confidence: 99%

“…h-IoT devices may generate personal health and activity data of extraordinary granularity and unparalleled scope, creating windows into the everyday, but private lives of individuals that are accessible by third parties for analysis purposes (Mittelstadt, 2017). Of particular concern are the protection of individual privacy, information manipulation, information leaks, loss of information and misuse (Grundy et al, 2017;Mittelstadt, 2017;Kazlouski et al, 2020). It is therefore prudent to evaluate the data sharing phenomena, for both research and commercial applications.…”

Section: Introductionmentioning

confidence: 99%

“…Of particular concern are the protection of individual privacy, information manipulation, information leaks, loss of information and misuse (Grundy et al. , 2017; Mittelstadt, 2017; Kazlouski et al. , 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Earlier work has been done to identify vulnerabilities and entities in the h-IoT ecosystem that facilitate data sharing, as well as the quantification of data volumes and data types that are shared within this ecosystem (Fereidooni et al, 2017;Grundy et al, 2017Grundy et al, , 2019aKazlouski et al, 2020;Tangari et al, 2021). Although both Grundy et al (2017) and Grundy et al (2019a) were able to identify important entities and sharing pathways in subsystems of the h-IoT ecosystem using network analysis techniques, neither assessed the dynamic spreading capability of the networks.…”

Section: Introductionmentioning

confidence: 99%

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The structure and information spread capability of the network formed by integrated fitness apps

Vermeulen

Grobbelaar

2022

ITP

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PurposeIn this article we aim to understand how the network formed by fitness tracking devices and associated apps as a subset of the broader health-related Internet of things is capable of spreading information.Design/methodology/approachThe authors used a combination of a content analysis, network analysis, community detection and simulation. A sample of 922 health-related apps (including manufacturers' apps and developers) were collected through snowball sampling after an initial content analysis from a Google search for fitness tracking devices.FindingsThe network of fitness apps is disassortative with high-degree nodes connecting to low-degree nodes, follow a power-law degree distribution and present with low community structure. Information spreads faster through the network than an artificial small-world network and fastest when nodes with high degree centrality are the seeds.Practical implicationsThis capability to spread information holds implications for both intended and unintended data sharing.Originality/valueThe analysis confirms and supports evidence of widespread mobility of data between fitness and health apps that were initially reported in earlier work and in addition provides evidence for the dynamic diffusion capability of the network based on its structure. The structure of the network enables the duality of the purpose of data sharing.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Integrated Fitness Appsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The structure and information spread capability of the network formed by integrated fitness apps

Vermeulen

Grobbelaar

2022

ITP

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Enhancing user awareness on inferences obtained from fitness trackers data

Kounoudes

Kapitsaki

Katakis

2023

User Model User-Adap Inter

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In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data.

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Do You Know Who Is Talking to Your Wearable Smartband?

Cited by 2 publications

References 6 publications

The structure and information spread capability of the network formed by integrated fitness apps

The structure and information spread capability of the network formed by integrated fitness apps

Enhancing user awareness on inferences obtained from fitness trackers data

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