The digital harms of smart home devices: A systematic literature review
- URL: http://arxiv.org/abs/2209.05458v1
- Date: Tue, 30 Aug 2022 10:18:44 GMT
- Title: The digital harms of smart home devices: A systematic literature review
- Authors: David Buil-Gil, Steven Kemp, Stefanie Kuenzel, Lynne Coventry, Sameh
Zakhary, Daniel Tilley and James Nicholson
- Abstract summary: PRISMA methodology is used to systematically review 63 studies published between January 2011 and October 2021.
Published literature identifies that smart homes may pose threats to confidentiality (unwanted release of information), authentication (sensing information being falsified) and unauthorised access to system controls.
Other types of harms that are less common in the literature include hacking, malware and DoS attacks.
- Score: 3.786790434630697
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The connection of home electronic devices to the internet allows remote
control of physical devices and involves the collection of large volumes of
data. With the increase in the uptake of Internet-of-Things home devices, it
becomes critical to understand the digital harms of smart homes. We present a
systematic literature review on the security and privacy harms of smart homes.
PRISMA methodology is used to systematically review 63 studies published
between January 2011 and October 2021; and a review of known cases is
undertaken to illustrate the literature review findings with real-world
scenarios. Published literature identifies that smart homes may pose threats to
confidentiality (unwanted release of information), authentication (sensing
information being falsified) and unauthorised access to system controls. Most
existing studies focus on privacy intrusions as a prevalent form of harm
against smart homes. Other types of harms that are less common in the
literature include hacking, malware and DoS attacks. Digital harms, and data
associated with these harms, may vary extensively across smart devices. Most
studies propose technical measures to mitigate digital harms, while fewer
consider social prevention mechanisms. We also identify salient gaps in
research, and argue that these should be addressed in future cross-disciplinary
research initiatives.
Related papers
- Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training.
Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs.
This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
arXiv Detail & Related papers (2024-11-15T08:09:28Z) - Cyberbullying Detection: Exploring Datasets, Technologies, and Approaches on Social Media Platforms [3.235558067839701]
This paper presents a comprehensive systematic review of studies conducted on cyberbullying detection.
It explores existing studies, proposed solutions, identified gaps, datasets, technologies, approaches, challenges, and recommendations.
arXiv Detail & Related papers (2024-05-22T04:58:20Z) - Conceptualising an Anti-Digital Forensics Kill Chain for Smart Homes [0.0]
This paper delineates the application of Anti-Digital Forensics in Smart Home scenarios.
It argues, in response, the conceptualisation of an ADF Kill Chain tailored to Smart Home ecosystems.
arXiv Detail & Related papers (2023-12-23T10:31:36Z) - On the Privacy of Mental Health Apps: An Empirical Investigation and its
Implications for Apps Development [14.113922276394588]
This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps.
We analyzed 27 top-ranked mental health apps from Google Play Store.
The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests.
arXiv Detail & Related papers (2022-01-22T09:23:56Z) - A Systematic Literature Review on Wearable Health Data Publishing under
Differential Privacy [2.099922236065961]
Wearable devices generate different types of physiological data about the individuals.
Differential Privacy (DP) has emerged as a proficient technique to publish privacy sensitive data.
arXiv Detail & Related papers (2021-09-15T14:43:00Z) - Threat of Adversarial Attacks on Deep Learning in Computer Vision:
Survey II [86.51135909513047]
Deep Learning is vulnerable to adversarial attacks that can manipulate its predictions.
This article reviews the contributions made by the computer vision community in adversarial attacks on deep learning.
It provides definitions of technical terminologies for non-experts in this domain.
arXiv Detail & Related papers (2021-08-01T08:54:47Z) - Inspect, Understand, Overcome: A Survey of Practical Methods for AI
Safety [54.478842696269304]
The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings.
In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged.
Our paper addresses both machine learning experts and safety engineers.
arXiv Detail & Related papers (2021-04-29T09:54:54Z) - Individual Explanations in Machine Learning Models: A Survey for
Practitioners [69.02688684221265]
The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise.
Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways.
Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
arXiv Detail & Related papers (2021-04-09T01:46:34Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Are Smart Home Devices Abandoning IPV Victims? [0.8029049649310213]
We show that domestic abuse and Intimate Partner Violence (IPV) in smart homes is more effective and less risky for abusers.
Victims find it more harmful and more challenging to protect themselves from.
We propose Desirable properties to design abuse-resistant smart home devices.
arXiv Detail & Related papers (2020-08-15T00:43:15Z) - COVI White Paper [67.04578448931741]
Contact tracing is an essential tool to change the course of the Covid-19 pandemic.
We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
arXiv Detail & Related papers (2020-05-18T07:40:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.