This paper first introduces a possible evolution of secure personal identification devices, based on RFID technology in the mobiles phones (NFC). Given the characteristics of the mobile phone market, this trend could grow quickly and importantly. This paper considers the possible impact of this evolution in term of privacy, focusing on a typical and important case: payment transactions. This paper sticks to the general approach and role of “card payment system”. Yet, it demonstrates that it is possible to improve some of the privacy characteristics of this kind of application. It also outlines the way payment protocol should be designed in order to reach this goal.

Transitioning edge and cloud computing in 5G networks towards service-based architecture increases their complexity as they become even more dynamic and intertwine more actors or delegation levels. In this paper, we demonstrate the Liability-aware security manager Analysis Service (LAS), a framework that uses machine learning techniques to compute liability and trust indicators for service-based architectures such as cloud microservices. Based on the commitments of Service Providers (SPs) and real-time observations collected by a Root Cause Analysis (RCA) tool GRALAF, the LAS computes three categories of liability and trust indicators, specifically, a Commitment Trust Score, Financial Exposure, and Commitment Trends.

Microservices architecture has become an increasingly common approach for building complex software systems. With the distributed nature of microservices, multiple actors can contribute to a service, hence affecting the dynamics of the environment and making the management of liabilities and trust more challenging. Service-Level Agreements (SLAs) are critical in that regard and any SLA violation or breach can result in significant financial damages. One major challenge is the lack of indicators to handle the liability and trust in such architectures. To address this issue, in this paper we propose a liability and trust analysis framework, namely the LASM Analysis Service (LAS), for multi-actor dynamic microservices that employs Machine Learning (ML) techniques.

Despite growing efforts and encouraging successes in recent decades, fully formally verified projects are still rare in the industrial landscape. The industry often lacks the tools and methodologies to efficiently scale the proof development process. In this work, we give a comprehensible overview of the proof development process for proof developers and project managers. The goal is to support proof developers by rationalizing the proof development process, which currently relies heavily on their intuition and expertise, and by facilitating communication with the management line. To this end, we concentrate on the aspect of proof manufacturing and highlight the most significant sources of proof effort. We propose means to mitigate the latter through proof practices (proof structuring, proof strategies, and proof planning), proof metrics, and tools. Our approach is project-agnostic, independent of specific proof expertise, and computed estimations do not assume prior similar developments. We evaluate our guidelines using a separation kernel undergoing formal verification, driving the proof process in an optimised way. Feedback from a project manager unfamiliar with proof development confirms the benefits of detailed planning of the proof development steps, clear progress communication to the hierarchy line, and alignment with established practices in the software industry.