Publikationer från Malmö universitet
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Is Your Home Becoming a Spy?: A Data-Centered Analysis and Classification of Smart Connected Home Systems
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0003-0546-072X
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-8512-2976
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0003-0998-6585
2020 (Engelska)Ingår i: IoT '20: Proceedings of the 10th International Conference on the Internet of Things, New York, United States: ACM Digital Library, 2020, artikel-id 17Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Smart connected home systems bring different privacy challenges to residents. The contribution of this paper is a novel privacy grounded classification of smart connected home systems that is focused on personal data exposure. This classification is built empirically through k-means cluster analysis from the technical specification of 81 commercial Internet of Things (IoT) systems as featured in PrivacyNotIncluded – an online database of consumer IoT systems. The attained classification helps us better understand the privacy implications and what is at stake with different smart connected home systems. Furthermore, we survey the entire spectrum of analyzed systems for their data collection capabilities. Systems were classified into four tiers: app-based accessors, watchers, location harvesters, and listeners, based on the sensing data the systems collect. Our findings indicate that being surveilled inside your home is a realistic threat, particularly, as the majority of the surveyed in-home IoT systems are installed with cameras, microphones, and location trackers. Finally, we identify research directions and suggest some best practices to mitigate the threat of in-house surveillance.

Ort, förlag, år, upplaga, sidor
New York, United States: ACM Digital Library, 2020. artikel-id 17
Nyckelord [en]
IoT, smart home, home automation, privacy, unsupervised classification, survey, web mining
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:mau:diva-18599DOI: 10.1145/3410992.3411012Scopus ID: 2-s2.0-85123040173ISBN: 978-1-4503-8758-3 (tryckt)OAI: oai:DiVA.org:mau-18599DiVA, id: diva2:1474865
Konferens
IoT '20
Tillgänglig från: 2020-10-10 Skapad: 2020-10-10 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Ingår i avhandling
1. On Privacy and Security in Smart Connected Homes
Öppna denna publikation i ny flik eller fönster >>On Privacy and Security in Smart Connected Homes
2021 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Malmö: Malmö universitet, 2021. s. 66
Serie
Studies in Computer Science
Nyckelord
smart connected homes, Internet of Things, smart homes devices, smart home data, threat identification, risk analysis, privacy, security, vulnerability assessment, mitigations, threat agents
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-39619 (URN)10.24834/isbn.9789178771646 (DOI)978-91-7877-163-9 (ISBN)978-91-7877-164-6 (ISBN)
Disputation
2021-01-11, D138 Orkanen och Zoom, Malmö University, Malmö, 13:15 (Engelska)
Opponent
Handledare
Anmärkning

Note: The papers are not included in the fulltext online

Tillgänglig från: 2021-01-21 Skapad: 2021-01-21 Senast uppdaterad: 2024-03-04Bibliografiskt granskad

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Förlagets fulltextScopushttps://dl.acm.org/doi/10.1145/3410992.3411012

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Bugeja, JosephJacobsson, AndreasDavidsson, Paul

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