Exploring How Privacy and Security Factor into IoT Device Purchase Behavior

Emami-Naeini, Pardis; Dixon, Henry; Agarwal, Yuvraj; Cranor, Lorrie Faith

doi:10.1145/3290605.3300764

Cited by 139 publications

(87 citation statements)

References 33 publications

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“…As our study was primarily qualitative, we do not report exact numbers when presenting most of our study findings. However, following recent qualitative work at CHI [6], we adopted the terminology presented in Figure 1 to provide a relative sense of frequency of major themes.…”

Section: Discussionmentioning

confidence: 99%

"It's a scavenger hunt": Usability of Websites' Opt-Out and Data Deletion Choices

Habib

Pearman

Wang

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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We conducted an in-lab user study with 24 participants to explore the usefulness and usability of privacy choices offered by websites. Participants were asked to find and use choices related to email marketing, targeted advertising, or data deletion on a set of nine websites that differed in terms of where and how these choices were presented. They struggled with several aspects of the interaction, such as selecting the correct page from a site's navigation menu and understanding what information to include in written opt-out requests. Participants found mechanisms located in account settings pages easier to use than options contained in privacy policies, but many still consulted help pages or sent email to request assistance. Our findings indicate that, despite their prevalence, privacy choices like those examined in this study are difficult for consumers to exercise in practice. We provide design and policy recommendations for making these website opt-out and deletion choices more useful and usable for consumers.

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Section: Discussionmentioning

confidence: 99%

"It's a scavenger hunt": Usability of Websites' Opt-Out and Data Deletion Choices

Habib

Pearman

Wang

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…In addition, professional devices are often be pre-selected by their supervisors or simply inherited. This is an aspect of our study that differs strongly from the influential work of Emami-Naeini et al [11]. In addition, we find that: some employees are responsible for devices that they do not use themselves; devices in work environments not only manage private information, but often contain critical and confidential information that can jeopardize an organization's internal and external reputation and success; and employees worry about sanctions related to unsafe practices and data loss.…”

Section: Work Environmentmentioning

confidence: 53%

“…To do so, they consider a wide set of criteria, including features and price. Instead, device security and privacy criteria are often not instrumental in the selection of devices as users often do not have knowledge or information about their security [11]. Our findings in a professional context show that SAFER's risk assessments provoked significantly higher concern for business devices, as well as increased willingness to monitor those professional devices if they are considered vulnerable.…”

Section: Work Environmentmentioning

confidence: 84%

“…In fact, that approach relates to Emami-Naeini et al 's work [11]. In order to make device privacy and security information more accessible, they designed paper prototype labels for three smart devices and three label variants for each of those devices.…”

Section: Risk Assessment and Presentationmentioning

confidence: 99%

“…SAFER's user interface provides an extensive set of features, including a user's device overview, device assessment through descriptive text, various categories, traffic lights (Guided), detailed trends (Rich), additional descriptions through tooltips, and a comparison of devices across a specific category that is expected to aid device selection. That way, SAFER's user interface differs from related work that focused on single UI components like risk trees / maps [19], color-coded rating tables [25], and static labels [11]. In this section, we reflect on differences between SAFER's user interfaces and related work.…”

Section: Safer's User Interfaces (Ui) In the Wider Contextmentioning

confidence: 99%

See 2 more Smart Citations

Safer

Oser

Feger

Woźniak

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Users of Internet of Things (IoT) devices are often unaware of their security risks and cannot sufficiently factor security considerations into their device selection. This puts networks, infrastructure and users at risk. We developed and evaluated SAFER, an IoT device risk assessment framework designed to improve users' ability to assess the security of connected devices. We deployed SAFER in a large multinational organization that permits use of private devices. To evaluate the framework, we conducted a mixed-method study with 20 employees. Our findings suggest that SAFER increases users' awareness of security issues. It provides valuable advice and impacts device selection. Based on our findings, we discuss implications for the design of device risk assessment tools, with particular regard to the relationship between risk communication and user perceptions of device complexity. CCS Concepts: • Security and privacy → Usability in security and privacy; • Human-centered computing → Empirical studies in HCI; Ubiquitous and mobile computing systems and tools; Information visualization.

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An optimum security enabled System‐on‐Chip selection for Internet of Things applications using genetic algorithm modeling

Krishnamoorthy

Kalimuthu

2021

Concurrency and Computation

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Summary The System‐on‐Chip (SoC) is the core component in embedded systems that push toward the evolution of the Internet of Things. The quick growth of embedded SoC devices has a substantial influence to overcome security issues in embedded system design. Since microcontroller‐based systems are security and safety‐critical, this article focuses on security aspects of state‐of‐the‐art microcontrollers through performing design space analysis of several crypto‐core algorithms. These crypto functions are analyzed and compared to identify and implement the appropriate algorithm for on‐chip security. The genetic algorithm (GA) optimization model is utilized for the optimal selection of secured SoC from the commercial devices that are stored in the database. In this work, the fitness function is proposed with various levels such as performance‐centric, security enabled, low operating power, and application‐specific SoC selection. We also validate the GA results by implementing the security benchmarks on resultant devices by analyzing the execution time and performance. The accuracy of the GA‐based optimum SoC selection is estimated using confusion matrix method that produces the accuracy of 89.66%.

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Exploring How Privacy and Security Factor into IoT Device Purchase Behavior

Cited by 139 publications

References 33 publications

"It's a scavenger hunt": Usability of Websites' Opt-Out and Data Deletion Choices

"It's a scavenger hunt": Usability of Websites' Opt-Out and Data Deletion Choices

Safer

An optimum security enabled System‐on‐Chip selection for Internet of Things applications using genetic algorithm modeling

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