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Best-Practice-Based Framework for User-Centric Privacy-Preserving Solutions in Smart Home Environments

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Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2022)

Abstract

The rapid technological progress causes smart environments, such as smart homes, cities, etc., to become more ubiquitous in our daily lives. Privacy issues arise when the smart objects in those smart environments collect and disclose sensitive data without users’ consent. Therefore, existing works and the European General Data Protection Regulation (GDPR) are still calling for privacy-preserving solutions with more user involvement and automated decision-making. Existing works show research gaps regarding context-aware privacy-preference modellings. They do not present best-practice-based frameworks for user-centric privacy-preserving approaches allowing context-aware adapting of users’ privacy and data disclosure preferences while considering their past activities. Hence, this paper proposes a best-practice-based framework for user-centric privacy-preserving solutions with automation options. The proposed approach supplies users data sharing recommendations with minimum human interference while considering (1) GDPR requirements, (2) context-sensitive factors and (3) users’ past activities. The paper also outlines how the proposed framework can be integrated in an existing user-centric privacy-preserving approach in the future. In this way, the proposed approach can be integrated in the existing IoT architecture systems, which allow users to control the entire data collection, storage and disclosure process in smart home environments.

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Notes

  1. 1.

    From the second iteration the \(B_{d}\) and \(B_{di}\) do not contain the same data in case the users decide to adjust the data sharing recommendations.

  2. 2.

    “The setting options regarding data aggregation allow end users to choose between two options. The two options are (1) the exact time of each action of the smart object for daily review (\(t_{Act1}\)) or (2) the time period users want to aggregate and review the collected data by their smart objects (\(t_{Act2}\)), for example, weekly, monthly. An example for \(t_{Act1}\) could be that the smart object owner is absent at 07:30 am on the 5th of February and present again at 8 pm in the living room. He gets up at 06:30 am and switches on his smart bulbs in two rooms, namely the bathroom and sleeping room. In contrast to this, an example for \(t_{Act2}\) could be that the smart object owner is available at home at various times per month and switches on his smart bulbs 200 times per month.”  [45].

  3. 3.

    Examples for user details are age, country.

  4. 4.

    “The default settings for \(So_{Act}\) regarding data aggregation layer is assigned to \(So_{Act1}\), which means that the granularity of the data is set at the layer of sensors.” [45].

  5. 5.

    “\(dCon\) include third parties getting access to disclosed data, such as doctors, insurance company, government agencies, etc.” [45].

  6. 6.

    “... usage purposes informs end users for which purpose, such as personal health plan, statistical purposes, etc., the shared data are used by the \(dCon\)...” [45].

  7. 7.

    WEKA 3 is considered a very highly ranked top detection tool and data mining tool [35].

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Acknowledgments

We thank the anonymous reviewers for their feedback, and special thanks to Lindrit Kqiku, Alexandr Railean, Patrick Kühtreiber and Alexander Richter for the exchange and feedback.

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Authors and Affiliations

  1. Georg-August-University Göttingen, Göttinge, Germany

    Chathurangi Ishara Wickramasinghe

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  1. Chathurangi Ishara Wickramasinghe
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Correspondence to Chathurangi Ishara Wickramasinghe .

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  1. University of Pittsburgh, Pittsburgh, PA, USA

    Shangguan Longfei

  2. Microsoft Research, Redmond, WA, USA

    Priyantha Bodhi

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Wickramasinghe, C.I. (2023). Best-Practice-Based Framework for User-Centric Privacy-Preserving Solutions in Smart Home Environments. In: Longfei, S., Bodhi, P. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-34776-4_6

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  • DOI: https://doi.org/10.1007/978-3-031-34776-4_6

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