Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Privacy Protection in the Era of the Internet of Things
share announcement

Privacy Protection in the Era of the Internet of Things

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 8180

Special Issue Editor

Prof. Dr. Qingqi Pei

E-Mail Website
Guest Editor
School of Communication Engineering, Xidian University, Xi’an 710071, China
Interests: trusted computing network; internet of things and edge computing security; wireless network physical layer security; blockchain technology; distributed collaborative attack and defense technology; data security and privacy protection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the Internet of Things (IoT), as a critical stimulator for both science and economy by many countries, has been deployed on a large scale in fields such as intelligent transportation, manufacturing, medicine and health, and smart homes. The latest advances in computing, communication, and control technologies are driving a greater wave of the Internet of Things, which could lead to a tremendous amount of data being accumulated continuously. Virtual assistants, personal security cameras, wearable technology, and other smart devices mean that the data collected by technology companies is no longer just from our digital presence—data are collected from our homes, our cars, and our bodies.

Big data processing and analysis play increasingly important roles in making the world simpler, better, and smarter. Particularly, the valuable data extracted from data processing and analysis provides unprecedented opportunities for emerging applications. However, this also imposes new security, privacy, and trust challenges. Exposing private data, manipulating user behavior, and profiting at the expense of user privacy are becoming the economic lifeline of many companies. This Special Issue highlights the urgent need to develop novel methodologies to tackle these challenges.

Thus, this Special Issue will promote the state-of-the-art research covering all aspects of security, privacy, and trust in IoT as it relates to addressing the privacy protection challenges that pertain to this cutting-edge research topic. High-quality contributions addressing related theoretical and practical aspects are expected. Therefore, researchers are invited to submit their manuscripts to this Special Issue and contribute their models, proposals, reviews, and studies.

Prof. Dr. Qingqi Pei
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data security and privacy protection
  • Internet of Things and edge computing security
  • trusted computing network
  • wireless network physical layer security
  • distributed collaborative attack and defense technology
  • blockchain technology
  • fine-grained access control
  • secure data sharing

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

18 pages, 35186 KiB  
Article
Location-Visiting Characteristics Based Privacy Protection of Sensitive Relationships
by Xiu-Feng Xia, Miao Jiang, Xiang-Yu Liu and Chuan-Yu Zong
Electronics 2022, 11(8), 1214; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11081214 - 12 Apr 2022
Viewed by 1150
Abstract
In the era of Internet of Things (IoT), the problem of the privacy leakage of sensitive relationships is critical. This problem is caused by the spatial–temporal correlation between users in location-based social networks (LBSNs). To solve this problem, a sensitive relationship-protection algorithm based [...] Read more.
In the era of Internet of Things (IoT), the problem of the privacy leakage of sensitive relationships is critical. This problem is caused by the spatial–temporal correlation between users in location-based social networks (LBSNs). To solve this problem, a sensitive relationship-protection algorithm based on location-visiting characteristics is proposed in this paper. Firstly, a new model based on location-visiting characteristics is proposed for calculating the similarity between users, which evaluates check-in features of users and locations. In order to avoid an adversary inferring sensitive relationship privacy and to ensure the utility of data, our proposed algorithm adopts a heuristic rule to evaluate the impact of deduction contributions and information loss caused by data modifications. In addition, location-search technology is proposed to improve the algorithm’s execution efficiency. The experimental results show that our proposed algorithm can effectively protect the privacy of sensitive data. Full article
(This article belongs to the Special Issue Privacy Protection in the Era of the Internet of Things)
►▼ Show Figures

Figure 1

20 pages, 4869 KiB  
Article
A Novel Fragile Zero-Watermarking Algorithm for Digital Medical Images
by Zulfiqar Ali, Fazal-e-Amin and Muhammad Hussain
Electronics 2022, 11(5), 710; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11050710 - 25 Feb 2022
Cited by 6 | Viewed by 2638
Abstract
The wireless transmission of patients’ particulars and medical data to a specialised centre after an initial screening at a remote health facility may cause potential threats to patients’ data privacy and integrity. Although watermarking can be used to rectify such risks, it should [...] Read more.
The wireless transmission of patients’ particulars and medical data to a specialised centre after an initial screening at a remote health facility may cause potential threats to patients’ data privacy and integrity. Although watermarking can be used to rectify such risks, it should not degrade the medical data, because any change in the data characteristics may lead to a false diagnosis. Hence, zero watermarking can be helpful in these circumstances. At the same time, the transmitted data must create a warning in case of tampering or a malicious attack. Thus, watermarking should be fragile in nature. Consequently, a novel hybrid approach using fragile zero watermarking is proposed in this study. Visual cryptography and chaotic randomness are major components of the proposed algorithm to avoid any breach of information through an illegitimate attempt. The proposed algorithm is evaluated using two datasets: the Digital Database for Screening Mammography and the Mini Mammographic Image Analysis Society database. In addition, a breast cancer detection system using a convolutional neural network is implemented to analyse the diagnosis in case of a malicious attack and after watermark insertion. The experimental results indicate that the proposed algorithm is reliable for privacy protection and data authentication. Full article
(This article belongs to the Special Issue Privacy Protection in the Era of the Internet of Things)
►▼ Show Figures

Figure 1

Other

Jump to: Research

34 pages, 1719 KiB  
Technical Note
Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data
by Abdul Majeed, Safiullah Khan and Seong Oun Hwang
Electronics 2022, 11(9), 1449; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11091449 - 30 Apr 2022
Cited by 6 | Viewed by 3646
Abstract
Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications has been rapidly expanding across the globe, improving people’s lifestyles with data-driven services (i.e., recommendations, smart healthcare, etc.). The synergy between AI and big data has become imperative considering the drastic [...] Read more.
Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications has been rapidly expanding across the globe, improving people’s lifestyles with data-driven services (i.e., recommendations, smart healthcare, etc.). The synergy between AI and big data has become imperative considering the drastic growth in personal data stemming from diverse sources (cloud computing, IoT, social networks, etc.). However, when data meet AI at some central place, it invites unimaginable privacy issues, and one of those issues is group privacy. Despite being the most significant problem, group privacy has not yet received the attention of the research community it is due. Problem Statement: We study how to preserve the privacy of particular groups (a community of people with some common attributes/properties) rather than an individual in personal data handling (i.e., sharing, aggregating, and/or performing analytics, etc.), especially when we talk about groups purposely made by two or more people (with clear group identifying markers), for whom we need to protect their privacy as a group. Aims/Objectives: With this technical letter, our aim is to introduce a new dimension of privacy (e.g., group privacy) from technical perspectives to the research community. The main objective is to advocate the possibility of group privacy breaches when big data meet AI in real-world scenarios. Methodology: We set a hypothesis that group privacy (extracting group-level information) is a genuine problem, and can likely occur when AI-based techniques meet high dimensional and large-scale datasets. To prove our hypothesis, we conducted a substantial number of experiments on two real-world benchmark datasets using AI techniques. Based on the experimental analysis, we found that the likelihood of privacy breaches occurring at the group level by using AI techniques is very high when data are sufficiently large. Apart from that, we tested the parameter effect of AI techniques and found that some parameters’ combinations can help to extract more and fine-grained data about groups. Findings: Based on experimental analysis, we found that vulnerability of group privacy can likely increase with the data size and capacity of the AI method. We found that some attributes of people can act as catalysts in compromising group privacy. We suggest that group privacy should also be given due attention as individual privacy is, and robust tools are imperative to restrict implications (i.e., biased decision making, denial of accommodation, hate speech, etc.) of group privacy. Significance of results: The obtained results are the first step towards responsible data science, and can pave the way to understanding the phenomenon of group privacy. Furthermore, the results contribute towards the protection of motives/goals/practices of minor communities in any society. Concluding statement: Due to the significant rise in digitation, privacy issues are mutating themselves. Hence, it is vital to quickly pinpoint emerging privacy threats and suggest practical remedies for them in order to mitigate their consequences on human beings. Full article
(This article belongs to the Special Issue Privacy Protection in the Era of the Internet of Things)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop