Special Issue "Privacy Protection on Social Network Data"

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

Deadline for manuscript submissions: closed (31 May 2021).

Special Issue Editor

Dr. Abdessamad Imine
E-Mail Website
Guest Editor
Lorraine University and LORIA, Nancy Grand-Est, France
Interests: Social Networks; Privacy; Privacy Risk analysis; Anonymization

Special Issue Information

Dear Colleagues,

Most of Social Networks provide control functions to limit the visibility of certain data (such as contact list, comments, wall posts and pictures) to a specific user group. However, most of users are unaware of the risks associated with the publication and exchange of personal data on social networks. For example, publishing and sharing location information on Facebook could easily lead to a burglary. Privacy risks may appear either directly after online data publication (e.g. finding a user's phone number within a wall post) or indirectly through an inference of private information (e.g. deducing political orientation from some friendship relations). Accordingly, the risks due to data sharing are constantly increasing, allowing privacy attacks with unfortunate consequences, and making people very reticent to remain socially active (e.g. staying connected with friends and expanding friendship circle). To practice online social activities with greater confidence and less risk, it is imperative to devise methods and tools that allow users to control themselves the usage that their data can be destined to. These tools assist users to detect and minimize the dissemination of personal information.

The goal of this Special Issue is to advance forward research and reflect the current progress in dealing with the privacy issues in Social Networks (SN). Topics of interests include but are not limited to the following:

  • Definition and evaluation of novel privacy metrics for SN;
  • Threats and inference attacks in SN;
  • Methods to evaluate/predict privacy risks in SN;
  • Attributed graph mining for analysing SN users;
  • Digital forensics in SN;
  • Trust and reputation in SN;
  • Adaptive privacy protection in SN;
  • Anonymization techniques in SN;
  • Privacy preservation against adversarial machine learning attacks.

Dr. Abdessamad Imine
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 papers will be 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 1400 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

  • Social networks
  • Privacy Risks
  • Privacy engineering and tools
  • Privacy protection

Published Papers (3 papers)

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Article
Assessment of End-User Susceptibility to Cybersecurity Threats in Saudi Arabia by Simulating Phishing Attacks
Information 2020, 11(12), 547; https://0-doi-org.brum.beds.ac.uk/10.3390/info11120547 - 25 Nov 2020
Cited by 3 | Viewed by 1244
Abstract
Phishing attacks are cybersecurity threats that have become increasingly sophisticated. Phishing is a cyberattack that can be carried out using various approaches and techniques. Usually, an attacker uses trickery as well as fraudulent and disguised means to steal valuable personal information or to [...] Read more.
Phishing attacks are cybersecurity threats that have become increasingly sophisticated. Phishing is a cyberattack that can be carried out using various approaches and techniques. Usually, an attacker uses trickery as well as fraudulent and disguised means to steal valuable personal information or to deceive the victim into running malicious code, thereby gaining access and controlling the victim’s systems. This study focuses on evaluating the level of cybersecurity knowledge and cyber awareness in Saudi Arabia. It is aimed at assessing end-user susceptibility through three phishing attack simulations. Furthermore, we elaborate on some of the concepts related to phishing attacks and review the steps required to launch such attacks. Subsequently, we briefly discuss the tools and techniques associated with each attack simulation. Finally, a comprehensive analysis is conducted to assess and evaluate the results. Full article
(This article belongs to the Special Issue Privacy Protection on Social Network Data)
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Article
Preventative Nudges: Introducing Risk Cues for Supporting Online Self-Disclosure Decisions
Information 2020, 11(8), 399; https://0-doi-org.brum.beds.ac.uk/10.3390/info11080399 - 18 Aug 2020
Cited by 3 | Viewed by 1742 | Correction
Abstract
Like in the real world, perceptions of risk can influence the behavior and decisions that people make in online platforms. Users of Social Network Sites (SNSs) like Facebook make continuous decisions about their privacy since these are spaces designed to share private information [...] Read more.
Like in the real world, perceptions of risk can influence the behavior and decisions that people make in online platforms. Users of Social Network Sites (SNSs) like Facebook make continuous decisions about their privacy since these are spaces designed to share private information with large and diverse audiences. In particular, deciding whether or not to disclose such information will depend largely on each individual’s ability to assess the corresponding privacy risks. However, SNSs often lack awareness instruments that inform users about the consequences of unrestrained self-disclosure practices. Such an absence of risk information can lead to poor assessments and, consequently, undermine users’ privacy behavior. This work elaborates on the use of risk scenarios as a strategy for promoting safer privacy decisions in SNSs. In particular, we investigate, through an online survey, the effects of communicating those risks associated with online self-disclosure. Furthermore, we analyze the users’ perceived severity of privacy threats and its importance for the definition of personalized risk awareness mechanisms. Based on our findings, we introduce the design of preventative nudges as an approach for providing individual privacy support and guidance in SNSs. Full article
(This article belongs to the Special Issue Privacy Protection on Social Network Data)
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Correction
Correction: Díaz Ferreyra, N.E., et al. Preventative Nudges: Introducing Risk Cues for Supporting Online Self-Disclosure Decisions. Information 2020, 11, 399
Information 2020, 11(12), 555; https://0-doi-org.brum.beds.ac.uk/10.3390/info11120555 - 27 Nov 2020
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Abstract
After publication of the research paper [...] Full article
(This article belongs to the Special Issue Privacy Protection on Social Network Data)
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