Knowledge transfer and training objectives
ENCASE aims to create a strong industry-academia cooperation among its beneficiaries in the area of security and privacy in OSN’s with an emphasis on the protection of minors. During this cooperation researchers will be exchanged among the partners thus achieving exchange of knowledge and strengthening of collaboration among the academia.
Further, we will conduct interdisciplinary research in the intersection of user experience design, data mining and machine learning, and security and privacy. Specifically, we will develop a software suit of browser add-ons and the corresponding server-side software stack implementing security and privacy enhancing solutions with the goal of deployment over real world OSN’s. We aim to publish our results in the most influential academic venues. Also, our findings will assist in producing commercial offering in the area of social network security and privacy and to bring it to market using the four industrial partners.
Throughout the project, researchers will be able to enrich their existing background and expand their academic/business circle. This will be achieved through: a) active participation in ambitious research; b) exchange of researchers to different partners; and c) participation to networking events that will be organised by the project.
Research and innovation objectives
ENCASE aims to safeguard the security and privacy of minors against malicious actors in OSN’s, such as cyberbullies and online sexual abusers. To this end, the research and innovation objectives include:
- Understanding the security and privacy (S&P) concerns of OSN users with a focus on how they perceive the concept of S&P and what security-risky actions they may take.
- Research methods for the analysis of OSN information to perform user profiling, as well as sentiment and affective analysis. Here, we focus on minors and the aim is to alert users and OSN operators about child predators and cyberbullies, and to reveal users under distress.
- Design algorithms and machine learning techniques that exploit various OSN signals (such as likes and personal messages) to unveil fake information with emphasis on the role of online child abusers and their patterns for detection. We plan to extend the state of the art in fake account detection, audience boosting (e.g, fake likes) detection and false information propagation. Specifically, we will develop a production-grade open source OSN data analytics software stack that will facilitate the aforementioned operations. To this end, we will perform extensive measurement studies over proprietary and publicly crawlable data to assess the urgency and existence of such threads and to propose mitigation mechanisms.
- Design and implementation of usable web-based user interfaces to discourage users from befriending suspicious OSN users and warn the users or their custodians of when they are being or are about to be subjected to online abuse. Furthermore, the suggested interfaces should discourage users from sharing sensitive content with inappropriate audiences without the proper level of protection.
- Design and implementation of effective browser-based content protection (e.g., for photos or user profiles) mechanisms by employing watermarking, steganography or advanced encryption techniques.
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