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The End of Privacy?
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The End of Privacy?

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 25341

Special Issue Editor

Prof. Dr. Vic Grout

E-Mail Website
Guest Editor
Department of Computing, Wrexham Glyndŵr University, Plas Coch Campus, Mold Road, Wrexham LL11 2AW, UK
Interests: futurology; AI; big data; IoT; automation; technology ethics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We all know how hard technological forecasting can be. The technology itself, even in isolation, can be difficult to predict a few years into the future, but taking into account the wider social, legal, political, economic, environmental and demographic fallout, and throwing in some ethics and morality too, it becomes next to impossible. There’s too much to think about. Whilst some of us might have an idea of where, for example, the Internet of Things might be in five years’ time or, separately, artificial intelligence, robotics and automation, big data analytics, network connectivity, etc., putting it all together into a vision of this fully-automated, AI/big-data-driven, always-on/always-connected world is probably beyond most of us.

Thus the plan here is to focus on one issue that all these factors impact upon, personal privacy, and to pose a fairly simple question: Will it be possible to have personal data (secrets) in the world that future technology will bring us into? What possibilities (benefits and threats) will new technology open us up to? From individuals up to governments and corporations, how easily will information be shared and (how) can it be secured? To what extent can we realistically be protected by legislation? Where will politics and economics be brought to bear? Ultimately, what control will we have?

I’ve put together a deliberately provocative discussion paper at https://vicgrout.net/2018/11/16/no-more-privacy-any-more-just-putting-this-out-there/, in which I’ve outlined just one of a number of possible nightmare scenarios. What others are there? Does the outlook have to be this gloomy? Is there an alternative in which we can get this to work for the common good, without anything to fear?

The scope of this Special Issue if fairly wide and, I hope, should be attractive to researchers and futurists across many fields. We can consider emerging technologies, of course, but hopefully the wider social impact too. Please contact me directly if you’d like to discuss any ideas you have. I look forward to hearing from you and seeing where we go with this.

Prof. Dr. Vic Grout
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • Privacy
  • Personal data
  • Big data analytics
  • Internet of Things
  • Artificial intelligence

Published Papers (5 papers)

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Editorial

Jump to: Research

7 pages, 192 KiB  
Editorial
No More Privacy Any More?
by Vic Grout
Information 2019, 10(1), 19; https://doi.org/10.3390/info10010019 - 09 Jan 2019
Viewed by 3787
Abstract
The embodiment of the potential loss of privacy through a combination of artificial intelligence algorithms, big data analytics and Internet of Things technology might be something as simple, yet potentially terrifying, as an integrated app capable of recognising anyone, anytime, anywhere: effectively a [...] Read more.
The embodiment of the potential loss of privacy through a combination of artificial intelligence algorithms, big data analytics and Internet of Things technology might be something as simple, yet potentially terrifying, as an integrated app capable of recognising anyone, anytime, anywhere: effectively a global ‘Shazam for People’; but one additionally capable of returning extremely personal material about the individual. How credible is such a system? How many years away? And what might stop it? Full article
(This article belongs to the Special Issue The End of Privacy?)

Research

Jump to: Editorial

16 pages, 356 KiB  
Article
Social Media and the Scourge of Visual Privacy
by Jasmine DeHart, Makya Stell and Christan Grant
Information 2020, 11(2), 57; https://doi.org/10.3390/info11020057 - 21 Jan 2020
Cited by 11 | Viewed by 7121
Abstract
Online privacy has become immensely important with the growth of technology and the expansion of communication. Social Media Networks have risen to the forefront of current communication trends. With the current trends in social media, the question now becomes how can we actively [...] Read more.
Online privacy has become immensely important with the growth of technology and the expansion of communication. Social Media Networks have risen to the forefront of current communication trends. With the current trends in social media, the question now becomes how can we actively protect ourselves on these platforms? Users of social media networks share billions of images a day. Whether intentional or unintentional, users tend to share private information within these images. In this study, we investigate (1) the users’ perspective of privacy, (2) pervasiveness of privacy leaks on Twitter, and (3) the threats and dangers on these platforms. In this study, we incorporate techniques such as text analysis, analysis of variance, and crowdsourcing to process the data received from these sources. Based on the results, the participants’ definitions of privacy showed overlap regardless of age or gender identity. After looking at the survey results, most female participants displayed a heightened fear of dangers on social media networks because of threats in the following areas: assets and identity. When the participants were asked to rank the threats on social media, they showed a high concern for burglary and kidnapping. We find that participants need more education about the threats of visual content and how these privacy leaks can lead to physical, mental, and emotional danger. Full article
(This article belongs to the Special Issue The End of Privacy?)
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15 pages, 925 KiB  
Article
An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
by Yongwen Du, Gang Cai, Xuejun Zhang, Ting Liu and Jinghua Jiang
Information 2019, 10(9), 278; https://doi.org/10.3390/info10090278 - 05 Sep 2019
Cited by 9 | Viewed by 3749
Abstract
With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, [...] Read more.
With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, a variety of location privacy-preserving schemes have been recently proposed. Among these schemes, the dummy-based location privacy-preserving (DLP) scheme is a widely used approach to achieve location privacy for mobile users. However, the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices. Moreover, the DLP scheme is inadequate to resist against an adversary with side information. Thus, how to effectively select a dummy location is still a challenge. In this paper, we propose a novel lightweight dummy-based location privacy-preserving scheme, named the enhanced dummy-based location privacy-preserving(Enhanced-DLP) to address this challenge by considering both computational costs and side information. Specifically, the Enhanced-DLP adopts an improved greedy scheme to efficiently select dummy locations to form a k-anonymous set. A thorough security analysis demonstrated that our proposed Enhanced-DLP can protect user privacy against attacks. We performed a series of experiments to verify the effectiveness of our Enhanced-DLP. Compared with the existing scheme, the Enhanced-DLP can obtain lower computational costs for the selection of a dummy location and it can resist side information attacks. The experimental results illustrate that the Enhanced-DLP scheme can effectively be applied to protect the user’s location privacy in IoT applications and services. Full article
(This article belongs to the Special Issue The End of Privacy?)
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21 pages, 942 KiB  
Article
Encrypting and Preserving Sensitive Attributes in Customer Churn Data Using Novel Dragonfly Based Pseudonymizer Approach
by Kalyan Nagaraj, Sharvani GS and Amulyashree Sridhar
Information 2019, 10(9), 274; https://doi.org/10.3390/info10090274 - 31 Aug 2019
Cited by 5 | Viewed by 3832
Abstract
With miscellaneous information accessible in public depositories, consumer data is the knowledgebase for anticipating client preferences. For instance, subscriber details are inspected in telecommunication sector to ascertain growth, customer engagement and imminent opportunity for advancement of services. Amongst such parameters, churn rate is [...] Read more.
With miscellaneous information accessible in public depositories, consumer data is the knowledgebase for anticipating client preferences. For instance, subscriber details are inspected in telecommunication sector to ascertain growth, customer engagement and imminent opportunity for advancement of services. Amongst such parameters, churn rate is substantial to scrutinize migrating consumers. However, predicting churn is often accustomed with prevalent risk of invading sensitive information from subscribers. Henceforth, it is worth safeguarding subtle details prior to customer-churn assessment. A dual approach is adopted based on dragonfly and pseudonymizer algorithms to secure lucidity of customer data. This twofold approach ensures sensitive attributes are protected prior to churn analysis. Exactitude of this method is investigated by comparing performances of conventional privacy preserving models against the current model. Furthermore, churn detection is substantiated prior and post data preservation for detecting information loss. It was found that the privacy based feature selection method secured sensitive attributes effectively as compared to traditional approaches. Moreover, information loss estimated prior and post security concealment identified random forest classifier as superlative churn detection model with enhanced accuracy of 94.3% and minimal data forfeiture of 0.32%. Likewise, this approach can be adopted in several domains to shield vulnerable information prior to data modeling. Full article
(This article belongs to the Special Issue The End of Privacy?)
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15 pages, 2605 KiB  
Article
Personal Data Market Optimization Pricing Model Based on Privacy Level
by Jian Yang and Chunxiao Xing
Information 2019, 10(4), 123; https://doi.org/10.3390/info10040123 - 03 Apr 2019
Cited by 18 | Viewed by 5628
Abstract
In the era of the digital economy, data has become a new key production factor, and personal data represents the monetary value of a data-driven economy. Both the public and private sectors want to use these data for research and business. However, due [...] Read more.
In the era of the digital economy, data has become a new key production factor, and personal data represents the monetary value of a data-driven economy. Both the public and private sectors want to use these data for research and business. However, due to privacy issues, access to such data is limited. Given the business opportunities that have gaps between demand and supply, we consider establishing a private data market to resolve supply and demand conflicts. While there are many challenges to building such a data market, we only focus on three technical challenges: (1) How to provide a fair trading mechanism between data providers and data platforms? (2) What is the consumer’s attitude toward privacy data? (3) How to price personal data to maximize the profit of the data platform? In this paper, we first propose a compensation mechanism based on the privacy attitude of the data provider. Second, we analyze consumer self-selection behavior and establish a non-linear model to represent consumers’ willingness to pay (WTP). Finally, we establish a bi-level programming model and use genetic simulated annealing algorithm to solve the optimal pricing problem of personal data. The experimental results show that multi-level privacy division can meet the needs of consumers and maximize the profit of data platform. Full article
(This article belongs to the Special Issue The End of Privacy?)
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