Benefits and risks of smart home technologies

Smart homes are a priority area of strategic energy planning and national policy. The market adoption of smart home technologies (SHTs) relies on prospective users perceiving clear benefits with acceptable levels of risk. This paper characterises the perceived benefits and risks of SHTs from multiple perspectives. A representative national survey of UK homeowners (n=1025) finds prospective users have positive perceptions of the multiple functionality of SHTs including energy management. Ceding autonomy and independence in the home for increased technological control are the main perceived risks. An additional survey of actual SHT users (n=42) participating in a smart home field trial identifies the key role of early adopters in lowering perceived SHT risks for the mass market. Content analysis of SHT marketing material (n=62) finds the SHT industry are insufficiently emphasising measures to build consumer confidence on data security and privacy. Policymakers can play an important role in mitigating perceived risks, and supporting the energy-management potential of a smart-home future. Policy measures to support SHT market development include design and operating standards, guidelines on data and privacy, quality control, and in situ research programmes. Policy experiences with domestic energy efficiency technologies and with national smart meter roll-outs offer useful precedents

How Is the Internet of Things Industry Responding to the Cybersecurity Challenges of the Smart Home?

In this article, we investigate the privacy and security challenges of the smart home as perceived by the industry, with findings relating to cybersecurity awareness, transparency on legal data use, malicious data use, regulation issues, liability, and market incentives for cybersecurity; we also reveal how the industry has been responding to these challenges. Based on survey findings, we outlined a series of socio-technical challenges to smart home adoption. To understand these findings in more depth, we investigated qualitatively how these challenges were perceived and responded to by organizations in the Internet of Things (IoT) sector. We interviewed seven experts from six organizations involved in the design, development, or review of consumer IoT devices and services including both businesses and NGOs. Thematic analysis focused on two main themes, that is, responses to privacy and responses to security challenges of smart home adoption. Our study revealed that industry stakeholders are looking to address these adoption challenges by providing new technical solutions to mitigate the privacy and security risk of the smart home, producing new standards and influencing regulation, as well as building up communities of learning surrounding common issues. With this knowledge, industry stakeholders can take steps toward increasing smart home acceptability for consumers

Trust in Smart Homes: The Power of Social Influences and Perceived Risks

The increased reliance on smart technologies has caused people to consider smart homes. A smart home is using basic assistive devices to build a home environment that contains many technological features and home appliances that are connected and integrated. In order to adopt smart homes, it is needed that users trust this technology, and the factors that influence trust are yet to be discovered. Thus, the aim of this study is to gain a deeper understanding of customer trust in smart homes by empirically exploring the factors that influence customers’ trust in smart homes, understanding how those factors are intercorrelated, and the influence of power and direction of each factor. To address the research aim, an online survey is conducted to explore the perceptions of the residents of the UAE residents through a convenience sampling approach. As a result, most people believe that smart homes are reliable and competent. By collecting 158 responses and analyzing them through the SEM-PLS approach, it is found that the social influence, perceived security risks, and perceived financial risks significantly impact customers’ trust in smart homes and that the social influences can significantly impact people’s perceived risks (security, privacy, and financial risks) as mediators to trust in smart homes

Smart Home Personal Assistants: A Security and Privacy Review

Smart Home Personal Assistants (SPA) are an emerging innovation that is changing the way in which home users interact with the technology. However, there are a number of elements that expose these systems to various risks: i) the open nature of the voice channel they use, ii) the complexity of their architecture, iii) the AI features they rely on, and iv) their use of a wide-range of underlying technologies. This paper presents an in-depth review of the security and privacy issues in SPA, categorizing the most important attack vectors and their countermeasures. Based on this, we discuss open research challenges that can help steer the community to tackle and address current security and privacy issues in SPA. One of our key findings is that even though the attack surface of SPA is conspicuously broad and there has been a significant amount of recent research efforts in this area, research has so far focused on a small part of the attack surface, particularly on issues related to the interaction between the user and the SPA devices. We also point out that further research is needed to tackle issues related to authorization, speech recognition or profiling, to name a few. To the best of our knowledge, this is the first article to conduct such a comprehensive review and characterization of the security and privacy issues and countermeasures of SPA.Comment: Accepted for publication in ACM Computing Survey

Deep learning-based security behaviour analysis in IoT environments: A survey

Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security

Exploring the Use and Adoption of Smart Home Technology: Findings from Norway

Due to the continuously increasing socio-technical interconnectedness of the world, the massive increase in connected devices, networks, and systems creates new opportunities for automation and advanced digitalization like never before. With the perennial presence of smartphones, mobile technologies are also applied to and combined with new operations, including automation of domestic lives. Thus, smart and intelligent technologies is a hot topic in the smart home industry. Researchers have studied motivating and blocking factors for smart home technology adoptions among consumers. As Norway is a technologically developed country with generally skilled citizens, the Norwegian smart home market comprises a potential market for mass adoption of smart home technologies. To the researcher’s knowledge, there is little new literature on smart home technology adoption in Norway. Hence, this thesis will study drivers and barriers affecting Norwegian consumers’ intentions to adopt smart home technologies, and the diffusion of smart home adoption in the Norwegian market. Through a mixed-methods research design, this study provides insights from both a consumer and a professional perspective obtained from interviews, in addition to consumer insights from a survey. The data collection was based on a research model adapted from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) by Venkatesh et al. (2012). The research model used in this thesis consists of eight constructs which were measured by their effect on behavioral intention towards adoption smart home technology. The eight constructs include performance expectancy, effort expectancy, social influence, hedonic motivation, price value, facilitating conditions, energy management, and security and privacy. Through quantitative and qualitative data analysis, the findings showed that the strongest drivers that was identified for smart home technology adoption was hedonic motivation, price value, and social influence. The lack of awareness and familiarity of smart home technology was identified to be a central potential barrier to adoption, whereas enhanced market communication and education regarding smart home technology might contribute to get closer to mass adoption of smart home technology in Norway. The implications for practice entailed that smart home vendors should ensure a better communication of smart home technology’s benefits and usefulness towards consumer and assist in educating the mass market about smart home technology to raise awareness and familiarity. The implications for research pointed out that there is need for additional research on smart home technology adoption in Norway is needed in the future to see the development in the market. This thesis consists of six sections: (1) introduction to the research topic, objectives, and the research questions, (2) a literature review on existing relevant literature within the field of study, (3) research method, (4) reporting on empirical findings from the data collection and analysis, (5) discussion and implications of the respective findings in relation to the literature, and finally (6) conclusion, limitations, and suggested directions for future research

The Impacts of Privacy Rules on Users' Perception on Internet of Things (IoT) Applications: Focusing on Smart Home Security Service

Department of Management EngineeringAs communication and information technologies advance, the Internet of Things (IoT) has changed the way people live. In particular, as smart home security services have been widely commercialized, it is necessary to examine consumer perception. However, there is little research that explains the general perception of IoT and smart home services. This article will utilize communication privacy management theory and privacy calculus theory to investigate how options to protect privacy affect how users perceive benefits and costs and how those perceptions affect individuals??? intentions to use of smart home service. Scenario-based experiments were conducted, and perceived benefits and costs were treated as formative second-order constructs. The results of PLS analysis in the study showed that smart home options to protect privacy decreased perceived benefits and increased perceived costs. In addition, the perceived benefits and perceived costs significantly affected the intention to use smart home security services. This research contributes to the field of IoT and smart home research and gives practitioners notable guidelines.ope

User Perceptions of Smart Home IoT Privacy

Smart home Internet of Things (IoT) devices are rapidly increasing in popularity, with more households including Internet-connected devices that continuously monitor user activities. In this study, we conduct eleven semi-structured interviews with smart home owners, investigating their reasons for purchasing IoT devices, perceptions of smart home privacy risks, and actions taken to protect their privacy from those external to the home who create, manage, track, or regulate IoT devices and/or their data. We note several recurring themes. First, users' desires for convenience and connectedness dictate their privacy-related behaviors for dealing with external entities, such as device manufacturers, Internet Service Providers, governments, and advertisers. Second, user opinions about external entities collecting smart home data depend on perceived benefit from these entities. Third, users trust IoT device manufacturers to protect their privacy but do not verify that these protections are in place. Fourth, users are unaware of privacy risks from inference algorithms operating on data from non-audio/visual devices. These findings motivate several recommendations for device designers, researchers, and industry standards to better match device privacy features to the expectations and preferences of smart home owners.Comment: 20 pages, 1 tabl

Evaluating the Contextual Integrity of Privacy Regulation: Parents' IoT Toy Privacy Norms Versus COPPA

Increased concern about data privacy has prompted new and updated data protection regulations worldwide. However, there has been no rigorous way to test whether the practices mandated by these regulations actually align with the privacy norms of affected populations. Here, we demonstrate that surveys based on the theory of contextual integrity provide a quantifiable and scalable method for measuring the conformity of specific regulatory provisions to privacy norms. We apply this method to the U.S. Children's Online Privacy Protection Act (COPPA), surveying 195 parents and providing the first data that COPPA's mandates generally align with parents' privacy expectations for Internet-connected "smart" children's toys. Nevertheless, variations in the acceptability of data collection across specific smart toys, information types, parent ages, and other conditions emphasize the importance of detailed contextual factors to privacy norms, which may not be adequately captured by COPPA.Comment: 18 pages, 1 table, 4 figures, 2 appendice