The systemic dimension of success (or failure?) in the use of data and AI during the COVID-19 pandemic. A cross-country comparison on contact tracing apps

During the COVID-19 pandemic, public attention turned to contact tracing apps as a possible solution to the spread of the virus. Many countries have moved in this direction, adopting contact tracing apps, while respecting personal data protection and, for EU countries, adhering to a number of fundamental principles: voluntariness, interoperability, regulatory coverage, purpose specification, minimisation, transparency, protection, security, and timeliness. In spite of timely public policy efforts, tracking apps have not been a success in many countries, and today, when their use could be of great importance, it seems appropriate to open a reflection on the success and unsuccessfulness of a public policy that has resolutely supported the use of digital technologies for public utility purposes. This working paper proposes a comparative analysis of nine OECD countries: Australia, France, Germany, Ireland, Italy, New Zealand, Russia, South Korea, Spain. It outlines the specific factors in each country's public policy that made the use of tracking apps possible, in terms of policy design with respect to: objectives, instruments, public procurement selection criteria, resources and the context in which the policy was implemented. The working paper concludes with three lessons learned from the comparative analysis: the privacy paradox, the choice of a public interest technology, and the systemic interweaving that the implementation of a public policy must take into account to enhance the effectiveness of a public interest action

Malware detection at runtime for resource-constrained mobile devices: data-driven approach

The number of smart and connected mobile devices is increasing, bringing enormous possibilities to users in various domains and transforming everything that we get in touch with into smart. Thus, we have smart watches, smart phones, smart homes, and finally even smart cities. Increased smartness of mobile devices means that they contain more valuable information about their users, more decision making capabilities, and more control over sometimes even life-critical systems. Although, on one side, all of these are necessary in order to enable mobile devices maintain their main purpose to help and support people, on the other, it opens new vulnerabilities. Namely, with increased number and volume of smart devices, also the interest of attackers to abuse them is rising, making their security one of the main challenges. The main mean that the attackers use in order to abuse mobile devices is malicious software, shortly called malware. One way to protect against malware is by using static analysis, that investigates the nature of software by analyzing its static features. However, this technique detects well only known malware and it is prone to obfuscation, which means that it is relatively easy to create a new malicious sample that would be able to pass the radar. Thus, alone, is not powerful enough to protect the users against increasing malicious attacks. The other way to cope with malware is through dynamic analysis, where the nature of the software is decided based on its behavior during its execution on a device. This is a promising solution, because while the code of the software can be easily changed to appear as new, the same cannot be done with ease with its behavior when being executed. However, in order to achieve high accuracy dynamic analysis usually requires computational resources that are beyond suitable for battery-operated mobile devices. This is further complicated if, in addition to detecting the presence of malware, we also want to understand which type of malware it is, in order to trigger suitable countermeasures. Finally, the decisions on potential infections have to happen early enough, to guarantee minimal exposure to the attacks. Fulfilling these requirements in a mobile, battery-operated environments is a challenging task, for which, to the best of our knowledge, a suitable solution is not yet proposed. In this thesis, we pave the way towards such a solution by proposing a dynamic malware detection system that is able to early detect malware that appears at runtime and that provides useful information to discriminate between diverse types of malware while taking into account limited resources of mobile devices. On a mobile device we monitor a set of the representative features for presence of malware and based on them we trigger an alarm if software infection is observed. When this happens, we analyze a set of previously stored information relevant for malware classification, in order to understand what type of malware is being executed. In order to make the detection efficient and suitable for resource-constrained environments of mobile devices, we minimize the set of observed system parameters to only the most informative ones for both detection and classification. Additionally, since sampling period of monitoring infrastructure is directly connected to the power consumption, we take it into account as an important parameter of the development of the detection system. In order to make detection effective, we use dynamic features related to memory, CPU, system calls and network as they reflect well the behavior of a system. Our experiments show that the monitoring with a sampling period of eight seconds gives a good trade-off between detection accuracy, detection time and consumed power. Using it and by monitoring a set of only seven dynamic features (six related to the behavior of memory and one of CPU), we are able to provide a detection solution that satisfies the initial requirements and to detect malware at runtime with F- measure of 0.85, within 85.52 seconds of its execution, and with consumed average power of 20mW. Apart from observed features containing enough information to discriminate between malicious and benign applications, our results show that they can also be used to discriminate between diverse behavior of malware, reflected in different malware families. Using small number of features we are able to identify the presence of the malicious records from the considered family with precision of up to 99.8%. In addition to the standalone use of the proposed detection solution, we have also used it in a hybrid scenario where the applications were first analyzed by a static method, and it was able to detect correctly all the malware previously undetected by static analysis with false positive rate of 3.81% and average detection time of 44.72s. The method, we have designed, tested and validated, has been applied on a smartphone running on Android Operating System. However, since in the design of this method efficient usage of available computational resources was one of our main criteria, we are confident that the method as such can be applied also on the other battery-operated mobile devices of Internet of Things, in order to provide an effective and efficient system able to counter the ever-increasing and ever-evolving number and a variety of malicious attacks

Software Usability

This volume delivers a collection of high-quality contributions to help broaden developers’ and non-developers’ minds alike when it comes to considering software usability. It presents novel research and experiences and disseminates new ideas accessible to people who might not be software makers but who are undoubtedly software users

Quality of experience in affective pervasive environments

The confluence of miniaturised powerful devices, widespread communication networks and mass remote storage has caused a fundamental shift in the user interaction design paradigm. The distinction between system and user in pervasive environments is evolving into an increasingly integrated loop of interaction, raising a number of opportunities to provide enhanced and personalised experiences. We propose a platform, based on a smart architecture, to address the identified opportunities in pervasive computing. Smart systems aim at acting upon an environment for improving quality of experience: a subjective measure that has been defined as an emotional reaction to products or services. The inclusion of an emotional dimension allows us to measure individual user responses and deliver personalised services with the potential to influence experiences positively. The platform, Cloud2Bubble, leverages pervasive systems to aggregate user and environment data with the goal of addressing personal preferences and supra-functional requirements. This, combined with its societal implications, results in a set of design principles as a concrete fruition of design contractualism. In particular, this thesis describes: - a review of intelligent ubiquitous environments and relevant technologies, including a definition of user experience as a dynamic affective construct; - a specification of main components for personal data aggregation and service personalisation, without compromising privacy, security or usability; - the implementation of a software platform and a methodological procedure for its instantiation; - an evaluation of the developed platform and its benefits for urban mobility and public transport information systems; - a set of design principles for the design of ubiquitous systems, with an impact on individual experience and collective awareness. Cloud2Bubble contributes towards the development of affective intelligent ubiquitous systems with the potential to enhance user experience in pervasive environments. In addition, the platform aims at minimising the risk of user digital exposure while supporting collective action.Open Acces

A real-time framework for malicious behaviour discovery on android mobile devices.

openIn few years Android has become the most widespread operating system among mobile devices. Its extreme popularity combined with the personal information contained on smartphones - as financial account, private photos and other acquaintances’ data – has captured the attention of many criminal organizations and hackers. The consequence is the massive presence on the market of malwares targeting the Android architecture. A great amount of research has focused on mechanisms to discovery such threads analyzing the application package before installing it, looking for common patterns and specific features while other approaches try to discover the infection during the attack but the required computation penalizes the device’s performance and battery autonomy. In this thesis we present a novel framework for real-time monitoring the Android device’s behavior without compromising the user experience. Our approach, thanks to a client-server architecture, permits to know in time many information related to the system and the applications running on it. By defining appropriate rules through an ad-hoc language we are able to control the device’s behavior and understand if it is the result of an infection. Further, with the contribution of the server which collects data from many users, we are able to compare data from different devices and understand if an application is different from the “safe” version. During our tests we were able to discover if an application has been infected with the introduction of a malicious code and to understand if the device behavior deviates in time in respect to the user standard profile which was built dynamically over time.openInformaticaTaddeo, MarcoTaddeo, Marc

Fog computing for sustainable smart cities: a survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities

Analyzing & designing the security of shared resources on smartphone operating systems

Smartphone penetration surpassed 80% in the US and nears 70% in Western Europe. In fact, smartphones became the de facto devices users leverage to manage personal information and access external data and other connected devices on a daily basis. To support such multi-faceted functionality, smartphones are designed with a multi-process architecture, which enables third-party developers to build smartphone applications which can utilize smartphone internal and external resources to offer creative utility to users. Unfortunately, such third-party programs can exploit security inefficiencies in smartphone operating systems to gain unauthorized access to available resources, compromising the confidentiality of rich, highly sensitive user data. The smartphone ecosystem, is designed such that users can readily install and replace applications on their smartphones. This facilitates users’ efforts in customizing the capabilities of their smartphones tailored to their needs. Statistics report an increasing number of available smartphone applications— in 2017 there were approximately 3.5 million third-party apps on the official application store of the most popular smartphone platform. In addition we expect users to have approximately 95 such applications installed on their smartphones at any given point. However, mobile apps are developed by untrusted sources. On Android—which enjoys 80% of the smartphone OS market share—application developers are identified based on self-sign certificates. Thus there is no good way of holding a developer accountable for a malicious behavior. This creates an issue of multi-tenancy on smartphones where principals from diverse untrusted sources share internal and external smartphone resources. Smartphone OSs rely on traditional operating system process isolation strategies to confine untrusted third-party applications. However this approach is insufficient because incidental seemingly harmless resources can be utilized by untrusted tenants as side-channels to bypass the process boundaries. Smartphones also introduced a permission model to allow their users to govern third-party application access to system resources (such as camera, microphone and location functionality). However, this permission model is both coarse-grained and does not distinguish whether a permission has been declared by a trusted or an untrusted principal. This allows malicious applications to perform privilege escalation attacks on the mobile platform. To make things worse, applications might include third- party libraries, for advertising or common recognition tasks. Such libraries share the process address space with their host apps and as such can inherit all the privileges the host app does. Identifying and mitigating these problems on smartphones is not a trivial process. Manual analysis on its own of all mobile apps is cumbersome and impractical, code analysis techniques suffer from scalability and coverage issues, ad-hoc approaches are impractical and susceptible to mistakes, while sometimes vulnerabilities are well hidden at the interplays between smartphone tenants and resources. In this work I follow an analytical approach to discover major security and privacy issues on smartphone platforms. I utilize the Android OS as a use case, because of its open-source nature but also its popularity. In particular I focus on the multi-tenancy characteristic of smartphones and identify the re- sources each tenant within a process, across processes and across devices can access. I design analytical tools to automate the discovery process, attacks to better understand the adversary models, and introduce design changes to the participating systems to enable robust fine-grained access control of resources. My approach revealed a new understanding of the threats introduced from third-party libraries within an application process; it revealed new capabilities of the mobile application adversary exploiting shared filesystem and permission resources; and shows how a mobile app adversary can exploit shared communication mediums to compromise the confidentiality of the data collected by external devices (e.g. fitness and medical accessories, NFC tags etc.). Moreover, I show how we can eradicate these problems following an architectural design approach to introduce backward-compatible, effective and efficient modifications in operating systems to achieve fine-grained application access to shared resources. My work has let to security changes in the official release of Android by Google

Development of a Smart Lighting Android-based Application using Bluetooth Low Energy

The emergence of the Internet of Things (IoT) allowed new developments on home and building automation with devices that provide more power efficiency and adaptation to our needs. Therefore, this thesis presents a study about Blue- tooth Low-Energy and its application on a IoT context, through smart devices designed for home applications and to be integrated in smart home system. It is also investigated the advantages and disadvantages of Bluetooth Low Energy (BLE) over other communication protocols for IoT end-devices. State-of-art Smart Lighting Android-based Application using Bluetooth Low Energy (SLABLE) is implemented with BLE and covers three application layers: first a interactive mobile application for Android OS. Then the middleware to manage communication and the data gathered, implemented in a BLE built-in System-on-Chip (SoC) with the respective programming for tasks as sending and receiving informations or commands and an illumination automatic control, de- veloped in Arduino IDE. Lastly, an hardware layer that consists in sensors and a lamp dimming driver, to be integrated on a circuit board small enough to fit in already installed equipment boxes. The implemented system purpose is a transversal integration between all layers . Moreover, based on energy consumption study, it is shown that BLE modules are proven to be a good solution for IoT development due to their low-power consumption, also, for data exchange reliability and processing capacity to control and perform several actions at the same time.O aparecimento da Internet-das-Coisas(IoT) permitiu novos desenvolvimen- tos na área da automação para casas e edifícios com recurso a dispositivos que nos oferecem uma melhor eficiência energética e uma melhor adaptação às nossas necessidades. Desta forma, esta dissertação apresenta um estudo sobre Bluetooth Low-Energy e a sua aplicação no contexto da IoT, através de dispositivos inteli- gentes para aplicações domésticas e para integração em sistemas inteligentes. É também investigado as vantagens e desvantagens do mesmo face a outros proto- colos de comunicação para dispositivos IoT. O sistema doméstico inteligente e interactivo (SLABLE) apresentado no Estado- da-Arte abrange três camadas da implementação: primeiro uma aplicação móvel interativa para Android OS. A camada intermédia para gerir comunicações e reco- lha de dados, implementada num módulo SoC com BLE embutido, programado para desenvolver tarefas como enviar e receber dados ou instruções e o controlo automático da iluminação, desenvolvido em Arduino IDE. Por fim a última ca- mada consiste em sensores e um circuito de dimming para lâmpadas, para serem integrados numa PCB suficientemente pequena para caber em caixas de apare- lhagem. O objectivo do sistema implementado é a comunicação transversal entre todas as camadas. Além disso, com base no consumo de potência, mostra-se que os módulos BLE são uma boa solução para desenvolvimento de aplicações IoT devido ao seu baixo consumo energético, e também, fiabilidade da troca de dados e capacidade de processamento para controlar e realizar várias acções ao mesmo tempo

Preserving Users’ Location Privacy in Mobile Platforms

Mobile and interconnected devices both have witnessed rapid advancements in computing and networking capabilities due to the emergence of Internet-of-Things, Connected Societies, Smart Cities and other similar paradigms. Compared to traditional personal computers, these devices represent moving gateways that offer possibilities to influence new businesses and, at the same time, have the potential to exchange users’ sensitive data. As a result, this raises substantial threats to the security and privacy of users that must be considered. With the focus on location data, this thesis proposes an efficient and socially-acceptable solution to preserve users’ location privacy, maintaining the quality of service, and respecting the usability by not relying on changes to the mobile app ecosystem. This thesis first analyses the current mobile app ecosystem as to apply a privacy-bydesign approach to location privacy from the data computation to its visualisation. From our analysis, a 3-Layer Classification model is proposed that depicts the state-ofthe- art in three layers providing a new perspective towards privacy-preserving locationbased applications. Secondly, we propose a theoretically sound privacy-enhancing model, called LP-Cache, that forces the mobile app ecosystem to make location data usage patterns explicit and maintains the balance between location privacy and service utility. LP-Cache defines two location privacy preserving algorithms: on-device location calculation and personalised permissions. The former incorporates caching technique to determine the location of client devices by means of wireless access points and achieve data minimisation in the current process. With the later, users can manage each app and private place distinctly to mitigate fundamental location privacy threats, such as tracking, profiling, and identification. Finally, PL-Protector, implements LP-Cache as a middleware on Android platform. We evaluate PL-Protector in terms of performance, privacy, and security. Experimental results demonstrate acceptable delay and storage overheads, which are within practical limits. Hence, we claim that our approach is a practical, secure and efficient solution to preserve location privacy in the current mobile app ecosystem