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) | Viewed by 11507
Special Issue Editor
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
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Keywords
- Social networks
- Privacy Risks
- Privacy engineering and tools
- Privacy protection
