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On the Design of a Privacy-Centered Data Lifecycle for Smart Living Spaces
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0003-0546-072X
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-8512-2976
2020 (English)In: Privacy and Identity Management. Data for Better Living: AI and Privacy: 14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School, Windisch, Switzerland, August 19--23, 2019, Revised Selected Papers / [ed] Michael Friedewald, Melek Önen, Eva Lievens, Stephan Krenn, and Samuel Fricker, Springer, 2020, 576, p. 126-141Chapter in book (Refereed)
Abstract [en]

Many living spaces, such as homes, are becoming smarter and connected by using Internet of Things (IoT) technologies. Such systems should ideally be privacy-centered by design given the sensitive and personal data they commonly deal with. Nonetheless, few systematic methodologies exist that deal with privacy threats affecting IoT-based systems. In this paper, we capture the generic function of an IoT system to model privacy so that threats affecting such contexts can be identified and categorized at system design stage. In effect, we integrate an extension to so called Data Flow Diagrams (DFD) in the model, which provides the means to handle the privacy-specific threats in IoT systems. To demonstrate the usefulness of the model, we apply it to the design of a realistic use-case involving Facebook Portal. We use that as a means to elicit the privacy threats and mitigations that can be adopted therein. Overall, we believe that the proposed extension and categorization of privacy threats provide a useful addition to IoT practitioners and researchers in support for the adoption of sound privacy-centered principles in the early stages of the smart living design process.

Place, publisher, year, edition, pages
Springer, 2020, 576. p. 126-141
Series
IFIP Advances in Information and Communication Technology book series, ISSN 1868-4238, E-ISSN 1868-422X ; 576
Keywords [en]
IoT, Data lifecycle, Data Flow Diagrams, Data privacy, Privacy threats, Smart connected home, Smart living space, Facebook Portal
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-16962DOI: 10.1007/978-3-030-42504-3_9Scopus ID: 2-s2.0-85082383912ISBN: 978-3-030-42503-6 (print)ISBN: 978-3-030-42504-3 (electronic)OAI: oai:DiVA.org:mau-16962DiVA, id: diva2:1420657
Note

14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School, Windisch, Switzerland, August 19--23, 2019, Revised Selected Papers

Available from: 2020-03-31 Created: 2020-03-31 Last updated: 2024-02-05Bibliographically approved
In thesis
1. On Privacy and Security in Smart Connected Homes
Open this publication in new window or tab >>On Privacy and Security in Smart Connected Homes
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The growth and presence of heterogeneous sensor-equipped Internet-connected devices inside the home can increase efficiency and quality of life for the residents. Simultaneously, these devices continuously collect, process, and transmit data about the residents and their daily lifestyle activities to unknown parties outside the home. Such data can be sensitive and personal, leading to increasingly intimate insights into private lives. This data allows for the implementation of services, personalization support, and benefits offered by smart home technologies. Alas, there has been a surge of cyberattacks on connected home devices that essentially compromise privacy and security of the residents.

Providing privacy and security is a critical issue in smart connected homes. Many residents are concerned about unauthorized access into their homes and about the privacy of their data. However, it is typically challenging to implement privacy and security in a smart connected home because of its heterogeneity of devices, the dynamic nature of the home network, and the fact that it is always connected to the Internet, amongst other things. As the numbers and types of smart home devices are increasing rapidly, so are the risks with these devices. Concurrently, it is also becoming increasingly challenging to gain a deeper understand- ing of the smart home. Such understanding is necessary to build a more privacy-preserving and secure smart connected home. Likewise, it is needed as a precursor to perform a comprehensive privacy and security analysis of the smart home.

In this dissertation, we render a comprehensive description and account of the smart connected home that can be used for conducting risk analysis. In doing so, we organize the underlying smart home devices ac- cording to their functionality, identify their data-collecting capabilities, and survey the data types being collected by them. Such is done using the technical specification of commercial devices, including their privacy policies. This description is then leveraged for identifying threats and for analyzing risks present in smart connected homes. Such is done by analyzing both scholarly literature and examples from the industry, and leveraging formal modeling. Additionally, we identify malicious threat agents and mitigations that are relevant to smart connected homes. This is performed without limiting the research and results to a particular configuration and type of smart home.

This research led to three main findings. First, the majority of the surveyed commercial devices are collecting instances of sensitive and personal data but are prone to critical vulnerabilities. Second, there is a shortage of scientific models that capture the complexity and heterogeneity of real-world smart home deployments, especially those intended for privacy risk analysis. Finally, despite the increasing regulations and attention to privacy and security, there is a lack of proactive and integrative approaches intended to safeguard privacy and security of the residents. We contributed to addressing these three findings by developing a framework and models that enable early identification of threats, better planning for risk management scenarios, and mitigation of potential impacts caused by attacks before they reach the homes and compromise the lives of the residents.

Overall, the scientific contributions presented in this dissertation help deepen the understanding and reasoning about privacy and security concerns affecting smart connected homes, and contributes to advancing the research in the area of risk analysis as applied to such systems.

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2021. p. 66
Series
Studies in Computer Science
Keywords
smart connected homes, Internet of Things, smart homes devices, smart home data, threat identification, risk analysis, privacy, security, vulnerability assessment, mitigations, threat agents
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-39619 (URN)10.24834/isbn.9789178771646 (DOI)978-91-7877-163-9 (ISBN)978-91-7877-164-6 (ISBN)
Public defence
2021-01-11, D138 Orkanen och Zoom, Malmö University, Malmö, 13:15 (English)
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Note

Note: The papers are not included in the fulltext online

Available from: 2021-01-21 Created: 2021-01-21 Last updated: 2024-03-04Bibliographically approved

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Bugeja, JosephJacobsson, Andreas

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