Personalized Privacy Assistants for the Internet of Things: Providing Users with Notice and Choice

Das, Anupam; Degeling, Martin; Smullen, Daniel; Sadeh, Norman

doi:10.1109/mprv.2018.03367733

Cited by 79 publications

(58 citation statements)

References 7 publications

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“…This is precisely where our modeling work fits in, so that consumer privacy preferences are in line with business practices of smart home developers. This creates a design space for consumer-facing (e.g., [6,12]) and developerfacing tools (e.g. our own).…”

Section: Discussionmentioning

confidence: 99%

“What if?” Predicting Individual Users’ Smart Home Privacy Preferences and Their Changes

Barbosa

Park

Yao

et al. 2019

Proceedings on Privacy Enhancing Technologies

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Smart home devices challenge a long-held notion that the home is a private and protected place. With this in mind, many developers market their products with a focus on privacy in order to gain user trust, yet privacy tensions arise with the growing adoption of these devices and the risk of inappropriate data practices in the smart home (e.g., secondary use of collected data). Therefore, it is important for developers to consider individual user preferences and how they would change under varying circumstances, in order to identify actionable steps towards developing user trust and exercising privacy-preserving data practices. To help achieve this, we present the design and evaluation of machine learning models that predict (1) personalized allow/deny decisions for different information flows involving various attributes, purposes, and devices (AUC .868), (2) what circumstances may change original decisions (AUC .899), and (3) how much (US dollars) one may be willing to pay or receive in exchange for smart home privacy (RMSE 12.459). We show how developers can use our models to derive actionable steps toward privacy-preserving data practices in the smart home.

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Section: Discussionmentioning

confidence: 99%

“What if?” Predicting Individual Users’ Smart Home Privacy Preferences and Their Changes

Barbosa

Park

Yao

et al. 2019

Proceedings on Privacy Enhancing Technologies

View full text Add to dashboard Cite

show abstract

“…Particularly when there are multiple devices which collaboratively define the service, an overarching consent needs to be given by aggregating and consolidating different data sources and devices. As such, the typical approach is to define new dedicated elements in a typical IoT architecture such as Agents [33], Privacy proxies on the IoT supporting network [34] or by creating a specific, network discoverable, service for Privacy disclosure (such as the Privacy Assistant of CMU [35]). This is made more challenging by the fact that fact that many IoT applications are dynamic, have lose connectivity graphs and weak (if any) device identity.…”

Section: Related Workmentioning

confidence: 99%

Towards an Accountable Web of Personal Information: The Web-of-Receipts

Jesus

2020

IEEE Access

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Consent is a corner stone in any Privacy practice or public policy. Much beyond a simple ''accept'' button, we show in this paper that obtaining and demonstrating valid Consent can be a complex matter since it is a multifaceted problem. This is important for both Organisations and Users. As shown in recent cases, not only cannot an individual prove what they accepted at any point in time, but also organisations are struggling with proving such consent was obtained leading to inefficiencies and noncompliance. To a large extent, this problem has not obtained sufficient visibility and research effort. In this paper, we review the current state of Consent and tie it to a problem of Accountability. We argue for a different approach to how the Web of Personal Information operates: the need of an accountable Web in the form of Personal Data Receipts which are able to protect both individuals and organisation. We call this evolution the Web-of-Receipts: online actions, from registration to real-time usage, is preceded by valid consent and is auditable (for Users) and demonstrable (for Organisations) at any moment by using secure protocols and locally stored artefacts such as Receipts. The key contribution of this paper is to elaborate on this unique perspective, present proof-of-concept results and lay out a research agenda. INDEX TERMS Privacy, consent, accountability, web-of-receipts, personal data receipts.

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“…Privacy assistants are meant to help users manage their privacy (Das et al 2018). These assistants can control and visualize the data streams from different devices and are capable of learning users' privacy preferences (Liu et al 2016).…”

Section: Users' Privacy Perceptions and Privacy Assistancementioning

confidence: 99%

User preferences for privacy features in digital assistants

et al. 2020

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Digital assistants (DA) perform routine tasks for users by interacting with the Internet of Things (IoT) devices and digital services. To do so, such assistants rely heavily on personal data, e.g. to provide personalized responses. This leads to privacy concerns for users and makes privacy features an important component of digital assistants.This study examines user preferences for three attributes of the design of privacy features in digital assistants, namely (1) the amount of information on personal data that is shown to the user, (2) explainability of the DA’s decision, and (3) the degree of gamification of the user interface (UI). In addition, it estimates users’ willingness to pay (WTP) for different versions of privacy features.The results for the full sample show that users prefer to understand the rationale behind the DA’s decisions based on the personal information involved, while being given information about the potential impacts of disclosing specific data. Further, the results indicate that users prefer to interact with the DA’s privacy features in a serious game. For this product, users are willing to pay €21.39 per month. In general, a playful design of privacy features is strongly preferred, as users are willing to pay 23.8% more compared to an option without any gamified elements. A detailed analysis identifies two customer clusters “Best Agers” and “DA Advocates”, which differ mainly in their average age and willingness to pay. Further, “DA Advocates” are mainly male and more privacy sensitive, whereas “Best Agers” show a higher affinity for a playful design of privacy features.

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Personalized Privacy Assistants for the Internet of Things: Providing Users with Notice and Choice

Cited by 79 publications

References 7 publications

“What if?” Predicting Individual Users’ Smart Home Privacy Preferences and Their Changes

“What if?” Predicting Individual Users’ Smart Home Privacy Preferences and Their Changes

Towards an Accountable Web of Personal Information: The Web-of-Receipts

User preferences for privacy features in digital assistants

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