Visible to the public Biblio

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Corneci, Vlad-Mihai, Carabas, Costin, Deaconescu, Razvan, Tapus, Nicolae.  2019.  Adding Custom Sandbox Profiles to iOS Apps. 2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet). :1–5.
The massive adoption of mobile devices by both individuals and companies is raising many security concerns. The fact that such devices are handling sensitive data makes them a target for attackers. Many attack prevention mechanisms are deployed with a last line of defense that focuses on the containment principle. Currently, iOS treats each 3rd party application alike which may lead to security flaws. We propose a framework in which each application has a custom sandboxed environment. We investigated the current confinement architecture used by Apple and built a solution on top of it.
Jasim, Anwar Chitheer, Hassoon, Imad Ali, Tapus, Nicolae.  2019.  Cloud: privacy For Locations Based-services' through Access Control with dynamic multi-level policy. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). :1911–1916.

LBSs are Location-Based Services that provide certain service based on the current or past user's location. During the past decade, LBSs have become more popular as a result of the widespread use of mobile devices with position functions. Location information is a secondary information that can provide personal insight about one's life. This issue associated with sharing of data in cloud-based locations. For example, a hospital is a public space and the actual location of the hospital does not carry any sensitive information. However, it may become sensitive if the specialty of the hospital is analyzed. In this paper we proposed design presents a combination of methods for providing data privacy protection for location-based services (LBSs) with the use of cloud service. The work built in zero trust and we start to manage the access to the system through different levels. The proposal is based on a model that stores user location data in supplementary servers and not in non-trustable third-party applications. The approach of the present research is to analyze the privacy protection possibilities through data partitioning. The data collected from the different recourses are distributed into different servers according to the partitioning model based on multi-level policy. Access is granted to third party applications only to designated servers and the privacy of the user profile is also ensured in each server, as they are not trustable.