Contact tracing apps running on mobile devices promise to reduce the manual effort required for identifying infection chains and to increase the tracing accuracy in the presence of COVID-19. Since the beginning of the pandemic, several contract tracing apps have been proposed or deployed in practice by academia or academic-industrial consortia. While some of them rely on centralized approaches and bear high privacy risks, others are based on decentralized approaches aimed at addressing user privacy aspects. Google and Apple announced their joint effort of providing an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". The contact tracing feature seems to become an opt-in feature in mobile devices running iOS or Android. Some countries have already decided or are planning to base their contact tracing apps on GAP 1 .Several researchers have pointed out potential privacy and security risks related to most of the contact tracing approaches proposed until now, including those that claim privacy protection and are based on GAP. However, the question remains as how realistic these risks are. This report makes a first attempt towards providing empirical evidence in real-world scenarios for two such risks discussed in the literature: one concerning privacy, and the other one concerning security. In particular, we focus on a practical analysis of GAP, given that it is the foundation of several tracing apps, including apps such as the Swiss SwissCOVID, the Italian Immuni, and the German Corona-Warn-App. We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that principally can generate fake contacts with the potential of significantly affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can be easily used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). We hope that our findings provide valuable input in the process of testing and certifying contact tracing apps, e.g., as planned for the German Corona-Warn-App, ultimately guiding improvements for secure and privacypreserving design and implementation of digital contact tracing systems.
In the present era of ubiquitous digitization more and more services are becoming available online which is amplified by the Corona pandemic. The fast-growing mobile service market opens up new attack surfaces to the mobile service ecosystem. Hence, mobile service providers are faced with various challenges to protect their services and in particular the associated mobile apps. Defenses for apps are, however, often limited to (lightweight) application-level protection such as app hardening and monitoring and intrusion detection. Therefore, effective risk management is crucial to limit the exposure of mobile services to threats and potential damages caused by attacks.In this paper, we present FedCRI, a solution for sharing Cyber-Risk Intelligence (CRI). At its core, FedCRI transforms mobile cyber-risks into machine learning (ML) models and leverages ML-based risk management to evaluate security risks on mobile devices. FedCRI enables fast and autonomous sharing of actionable ML-based CRI knowledge by utilizing Federated Learning (FL). FL allows collaborative training of effective risk detection models based on information contributed by different mobile service providers while preserving the privacy of the training data of the individual organizations. We extensively evaluate our approach on several real-world user databases representing 23.8 million users of security-critical mobile apps (since Android 4 and iOS 6) provided by nine different service providers in different European countries. The datasets were collected over the course of six years in the domains of financial services, payments, and insurances. Our approach can successfully extract accurate CRI models, allowing effective identification of cybersecurity risks on mobile devices. Our evaluation shows that the federated risk detection model can achieve better than 99% accuracy in terms of F1-score in most risk classification tasks with a very low number of false positives.
The COVID-19 pandemic has caused many countries to deploy novel digital contact tracing (DCT) systems to boost the efficiency of manual tracing of infection chains. In this paper, we systematically analyze DCT solutions and categorize them based on their design approaches and architectures. We analyze them with regard to effectiveness, security, privacy and ethical aspects and compare prominent solutions with regard to these requirements. In particular, we discuss shortcomings of the Google and Apple Exposure Notification API (GAEN) that is currently widely adopted all over the world. We find that the security and privacy of GAEN has considerable deficiencies as it can be compromised by severe large-scale attacks.We also discuss other proposed approaches for contact tracing, including our proposal TRACECORONA, that are based on Diffie-Hellman (DH) key exchange and aim at tackling shortcomings of existing solutions. Our extensive analysis shows that TRACECORONA fulfills the above security requirements better than deployed state-of-the-art approaches. We have implemented TRACECORONA and its beta test version has been used by more than 2000 users without any major functional problems 1 , demonstrating that there are no technical reasons requiring to make compromises with regard to the requirements of DCT approaches.
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