Abstract
The multitude of applications and security configurations of mobile devices requires automated approaches for effective user privacy protection. Current permission managers, the core mechanism for privacy protection in smartphones, have shown to be ineffective by failing to account for privacy's contextual dependency and personal preferences within context. In this paper we focus on the relation between privacy decisions (e.g. grant or deny a permission request) and their surrounding context, through an analysis of a real world dataset obtained in campaigns with 93 users. We leverage such findings and the collected data to develop methods for automated, personalized and context-aware privacy protection, so as to predict users' preferences with respect to permission requests. Our analysis reveals that while contextual features have some relevance in privacy decisions, the increase in prediction performance of using such features is minimal, since two features alone are capable of capturing a relevant effect of context changes, namely the category of the requesting application and the requested permission. Our methods for prediction of privacy preferences achieved an F1 score of 0.88, while reducing the number of privacy violations by 28% when compared to the standard Android permission manager.

Abstract
Along with the use of cloud-based services, infrastructure, and storage, the use of application logs in business critical applications is a standard practice. Application logs must be stored in an accessible manner in order to be used whenever needed. The debugging of these applications is a common situation where such access is required. Frequently, part of the information contained in logs records is sensitive. In this paper, we evaluate the possibility of storing critical logs in a remote storage while maintaining its confidentiality and server-side search capabilities. To the best of our knowledge, the designed search algorithm is the first to support full Boolean searches combined with field searching and nested queries. We demonstrate its feasibility and timely operation with a prototype implementation that never requires access, by the storage provider, to plain text information. Our solution was able to perform search and decryption operations at a rate of, approximately, 0.05 ms per line. A comparison with the related work allows us to demonstrate its feasibility and conclude that our solution is also the fastest one in indexing operations, the most frequent operations performed.

Abstract
Vulnerability scanning tools can help secure the computer networks of organisations. Triggered by the release of the Tsunami vulnerability scanner by Google, the authors analysed and compared the commonly used, free-to-use vulnerability scanners. The performance, accuracy and precision of these scanners are quite disparate and vary accordingly to the target systems. The computational, memory and network resources required be these scanners also differ. We present a recent and detailed comparison of such tools that are available for use by organisations with lower resources such as small and medium-sized enterprises. © 2021, IFIP International Federation for Information Processing.

Abstract
The Malware Information Sharing Platform (MISP) enables the sharing of cyberthreat information within a community, company or organisation. However, this platform presents limitations if its information is deemed as classified or shared only for a given period of time. This implies that this information should to be handled only in encrypted form. One solution is to use MISP with searchable encryption techniques to impose greater control over the sharing of information. In this paper, we propose a controlled information sharing functionality that features a synchronisation procedure that enables classified data exchange between MISP instances, based on policies and ensuring the required confidentiality and integrity of the shared data. Sequence charts are presented validating the configuration, the data synchronisation, and the data searching between multiple entities.

Abstract

Abstract
For the regularly medicated population, the management of the posology is of utmost importance. With increasing average life expectancy, people tend to become older and more likely to have chronic medical disorders, consequently taking more medicines. This is predominant in the older population, but it’s not exclusive to this generation. It’s a common problem for all those suffering from chronic diseases, regardless of age group. Performing a correct management of the medicines stock, as well as, taking them at the ideal time, is not always easy and, in some cases, the diversity of medicines needed to treat a particular medical disorder is a proof of that. Knowing what to take, how much to take, and ensuring compliance with the medication intervals, for each medication in use, becomes a serious problem for those who experience this reality. The situation is aggravated when the posology admits variable amounts, intervals, and combinations depending on the patient’s health condition. This paper presents a solution that optimizes the management of medication of users who use the services of institutions that provide health care to the elderly (e.g., day care centers or nursing homes). Making use of the NB-IoT network, artificial intelligence algorithms, a set of sensors and an Arduino MKR NB 1500, this solution, in addition to the functionalities already described, eHealthCare also has mechanisms that allow identifying the non-adherence to medication by the elderly. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.