Security and privacy issues for an IoT based smart home

Geneiatakis, Dimitris; Kounelis, Ioannis; Neisse, Ricardo; Nai-Fovino, Igor; Steri, Gary; Baldini, Gianmarco

doi:10.23919/mipro.2017.7973622

Cited by 156 publications

(68 citation statements)

References 7 publications

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“…From a more technical perspective, other researchers propose web services in order to raise SH user's privacy [17], encryption methods for preserving SH data privacy [18] or gateway architectures enhance users` privacy [19]. Other studies explored privacy hazards directly connected to the usage of an IoT based SH because of the networks between mobile devices of the users and SH infrastructure, like doors or windows (e.g., [2]).…”

Section: Sh and Related Literaturementioning

confidence: 99%

“…Increasingly, domestic appliances are mutually connected via information and communication technology, resulting in the emergence of smart homes (SH). Sensors, network connections and applications build an intelligent and interactive architecture aiming at increasing efficiency of everyday tasks and improving safety of homes and the people living in them [2]. A rising importance for SH environments with the potential to become an essential installation for everyday life is proposed by other studies [3].…”

Section: Introductionmentioning

confidence: 99%

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Privacy concerns in the smart home context

et al. 2020

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With the rapid development of the Internet of Things, intelligent systems are increasingly finding their way into everyday life and into people's homes. With the spread of these technologies, there is a growing concern about what sensitive data is collected and what it is used for. Unfortunately, the role of privacy has remained largely unexplored in the smart home (SH) usage context. This study addresses this gap in literature: to what extend users' concerns for information privacy influence the intended SH usage. This study takes an interactional psychology perspective and links the dimensions of the privacy concerns to the intention to use SH devices. Therefore, a multi-theoretical model using Smart PLS 3.2.8 was tested. This study contributes to the literature on privacy, and SH usage by exploring how and why privacy concerns influence participants' intended SH usage. The findings, derived from the empirical study emphasize the importance of addressing privacy concerns because they are capable of directly and indirectly influencing the intended SH usage. Finally, the results which indicate new directions for privacy and SH research are discussed and implications for research and practice are given.

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Section: Sh and Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy concerns in the smart home context

et al. 2020

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“…Dorri et al [50] proposed a blockchain-based smart home framework with respect to the fundamental security goals of confidentiality, integrity, and availability. Geneiatakis et al [51] employed a smart home IoT architecture to identify possible security and privacy issues for users. Through the above articles, we also plan, in future work, to focus more on making computation and communication practical for the encrypted data of smart homes.…”

Section: Introductionmentioning

confidence: 99%

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

Chen

Saiki

Nakamura

2020

Sensors

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To implement fine-grained context recognition that is accurate and affordable for general households, we present a novel technique that integrates multiple image-based cognitive APIs and light-weight machine learning. Our key idea is to regard every image as a document by exploiting "tags" derived by multiple APIs. The aim of this paper is to compare API-based models' performance and improve the recognition accuracy by preserving the affordability for general households. We present a novel method for further improving the recognition accuracy based on multiple cognitive APIs and four modules, fork integration, majority voting, score voting, and range voting.Our interest is to use little image data, applying cognitive services to implement affordable context sensing that can adapt to custom contexts in every single house. The cognitive service is a cloud service with cognitive computing functions that provide the capability to understand multimedia data (i.e., vision (object recognition) [19], speech recognition [20], natural language processing [21], etc.), based on sophisticated machine-learning algorithms powered by the offered big data and large-scale computing. They are offered by cloud companies, but the data processing algorithms behind them are not public. They are also widely used in various fields of research, such as modern knowledge management solutions [22] and criminal detection and recognition [23]. The cognitive API is an application program interface via the HTTP/REST protocol [24], with which developers can easily integrate powerful recognition features in their own applications. An image-based cognitive API receives an image from an external application, i.e., extracts specific information from the image, and returns the information in JavaScript Object Notation (JSON) [25] format from the cloud server rather than the local. The information usually contains a set of words called "tags", representing objects and concepts that the API has recognized in the given image. Examples of tags from the API are: [Living, room, indoors, classroom, basement, supporting structure]. The information of interest and the way of recognizing the image vary among individual cognitive services. Related work uses image tagging technology with deep learning, as in [26][27][28], but the implementation is more complex. In our future realistic implementation, for security and privacy of the users the images are not saved after sending the APIs over.The main contribution of this paper is to present a novel method which is not only affordable but also has higher accuracy in recognizing fine-grained home contexts. For this purpose, we are currently investigating techniques that integrate inexpensive camera devices, multiple image-based cognitive APIs, and light-weight machine learning. We previously encoded the tags of a single API to document vectors, then applied them into machine learning for the model construction [29]. However, we found that the accuracy significantly decreased for contexts with multiple people (e.g., "...

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“…Internet of Things (IoT) supports applications and services in smart homes and smart cities domains [1] . Therefore, risk examination of a smart home system designed and developed using an architecture.…”

Section: Introductionmentioning

confidence: 99%

“…EcoTwin 1 project announced about automating two trucks to drive closely behind each other, to approve cooperative automatic driving and to achieve safely by communicating with each other using a wireless connection. In addition, it benefits to reduce CO 2 emission (through fuel consumption), improved safety (through cameras, WiFi and a radar system) and improve traffic stream.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Traffic Vehicles (ITV) and Road Sign Detection Systems

Al-Barhamtoshy¹,

Abdelatif²,

ElBarahamtoshy³

2018

مجلة جامعة الملك عبدالعزيز-علوم الحاسبات وتقنية المعلومات

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This paper monitors and detects the neighbor's activities in smart vehicle using computer vision technology and innovation of Internet of Things (IoT). These activities include vehicles monitoring, movement, and location status of vehicle. The proposed solution recognized vehicles neighbors and collects data from whole sensors of vehicle, vehicle engine, road info, mobile of the derivers, and analyze such data to make some decisions. The decision includes vehicles monitoring, automated accident, road safety, air pollution, and congestion reduction in road. Therefore, the paper suggests a data analysis method to model many devices and engines, based on ontology formulation and IoT technology. This in addition, modeling and managing over such places information from anywhere at any time. Accordingly, the current paper investigate and focus on ideation and assessing the potential of computer vision using vehicles and roads signs detections, to control traffic congestion in smart roads. Consequently, an intelligent traffic approach (vehicles and road signs recognitions) will be presented in real smart vehicles or drivers assistant system. Several scenarios to identify potential components of ITVS to recognize neighbor's vehicles based on ontology architecture and road signs issues will be analyzed and recognized. The paper sketches out and develops an ontology-based to represent vehicle, drivers, roads, traffic and weather, so five ontologies will be drawn and presented. Vehicle detection ontology-based with Haar features and computer vision library will be implemented and tested. Although, traffic detection is used to help and warn drivers for specific actions. Therefore, driving safety is increased and it will be comfort. The recognition task of the vehicles and the detection task of signs within the road will be developed and tested.

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Security and privacy issues for an IoT based smart home

Cited by 156 publications

References 7 publications

Privacy concerns in the smart home context

Privacy concerns in the smart home context

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

Intelligent Traffic Vehicles (ITV) and Road Sign Detection Systems

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