ACM WiSec 2011 Poster and Demo Session

This edited article of MC2R features abstracts of posters and demos that were shown at the ACM Wireless Security Conference (ACM WiSec 2011)

Secure Outsourced Computation on Encrypted Data

Homomorphic encryption (HE) is a promising cryptographic technique that supports computations on encrypted data without requiring decryption first. This ability allows sensitive data, such as genomic, financial, or location data, to be outsourced for evaluation in a resourceful third-party such as the cloud without compromising data privacy. Basic homomorphic primitives support addition and multiplication on ciphertexts. These primitives can be utilized to represent essential computations, such as logic gates, which subsequently can support more complex functions. We propose the construction of efficient cryptographic protocols as building blocks (e.g., equality, comparison, and counting) that are commonly used in data analytics and machine learning. We explore the use of these building blocks in two privacy-preserving applications. One application leverages our secure prefix matching algorithm, which builds on top of the equality operation, to process geospatial queries on encrypted locations. The other applies our secure comparison protocol to perform conditional branching in private evaluation of decision trees. There are many outsourced computations that require joint evaluation on private data owned by multiple parties. For example, Genome-Wide Association Study (GWAS) is becoming feasible because of the recent advances of genome sequencing technology. Due to the sensitivity of genomic data, this data is encrypted using different keys possessed by different data owners. Computing on ciphertexts encrypted with multiple keys is a non-trivial task. Current solutions often require a joint key setup before any computation such as in threshold HE or incur large ciphertext size (at best, grows linearly in the number of involved keys) such as in multi-key HE. We propose a hybrid approach that combines the advantages of threshold and multi-key HE to support computations on ciphertexts encrypted with different keys while vastly reducing ciphertext size. Moreover, we propose the SparkFHE framework to support large-scale secure data analytics in the Cloud. SparkFHE integrates Apache Spark with Fully HE to support secure distributed data analytics and machine learning and make two novel contributions: (1) enabling Spark to perform efficient computation on large datasets while preserving user privacy, and (2) accelerating intensive homomorphic computation through parallelization of tasks across clusters of computing nodes. To our best knowledge, SparkFHE is the first addressing these two needs simultaneously

On Information-centric Resiliency and System-level Security in Constrained, Wireless Communication

The Internet of Things (IoT) interconnects many heterogeneous embedded devices either locally between each other, or globally with the Internet. These things are resource-constrained, e.g., powered by battery, and typically communicate via low-power and lossy wireless links. Communication needs to be secured and relies on crypto-operations that are often resource-intensive and in conflict with the device constraints. These challenging operational conditions on the cheapest hardware possible, the unreliable wireless transmission, and the need for protection against common threats of the inter-network, impose severe challenges to IoT networks. In this thesis, we advance the current state of the art in two dimensions. Part I assesses Information-centric networking (ICN) for the IoT, a network paradigm that promises enhanced reliability for data retrieval in constrained edge networks. ICN lacks a lower layer definition, which, however, is the key to enable device sleep cycles and exclusive wireless media access. This part of the thesis designs and evaluates an effective media access strategy for ICN to reduce the energy consumption and wireless interference on constrained IoT nodes. Part II examines the performance of hardware and software crypto-operations, executed on off-the-shelf IoT platforms. A novel system design enables the accessibility and auto-configuration of crypto-hardware through an operating system. One main focus is the generation of random numbers in the IoT. This part of the thesis further designs and evaluates Physical Unclonable Functions (PUFs) to provide novel randomness sources that generate highly unpredictable secrets, on low-cost devices that lack hardware-based security features. This thesis takes a practical view on the constrained IoT and is accompanied by real-world implementations and measurements. We contribute open source software, automation tools, a simulator, and reproducible measurement results from real IoT deployments using off-the-shelf hardware. The large-scale experiments in an open access testbed provide a direct starting point for future research

Security and Privacy for IoT Ecosystems

Smart devices have become an integral part of our everyday life. In contrast to smartphones and laptops, Internet of Things (IoT) devices are typically managed by the vendor. They allow little or no user-driven customization. Users need to use and trust IoT devices as they are, including the ecosystems involved in the processing and sharing of personal data. Ensuring that an IoT device does not leak private data is imperative. This thesis analyzes security practices in popular IoT ecosystems across several price segments. Our results show a gap between real-world implementations and state-of-the-art security measures. The process of responsible disclosure with the vendors revealed further practical challenges. Do they want to support backward compatibility with the same app and infrastructure over multiple IoT device generations? To which extent can they trust their supply chains in rolling out keys? Mature vendors have a budget for security and are aware of its demands. Despite this goodwill, developers sometimes fail at securing the concrete implementations in those complex ecosystems. Our analysis of real-world products reveals the actual efforts made by vendors to secure their products. Our responsible disclosure processes and publications of design recommendations not only increase security in existing products but also help connected ecosystem manufacturers to develop secure products. Moreover, we enable users to take control of their connected devices with firmware binary patching. If a vendor decides to no longer offer cloud services, bootstrapping a vendor-independent ecosystem is the only way to revive bricked devices. Binary patching is not only useful in the IoT context but also opens up these devices as research platforms. We are the first to publish tools for Bluetooth firmware and lower-layer analysis and uncover a security issue in Broadcom chips affecting hundreds of millions of devices manufactured by Apple, Samsung, Google, and more. Although we informed Broadcom and customers of their technologies of the weaknesses identified, some of these devices no longer receive official updates. For these, our binary patching framework is capable of building vendor-independent patches and retrofit security. Connected device vendors depend on standards; they rarely implement lower-layer communication schemes from scratch. Standards enable communication between devices of different vendors, which is crucial in many IoT setups. Secure standards help making products secure by design and, thus, need to be analyzed as early as possible. One possibility to integrate security into a lower-layer standard is Physical-Layer Security (PLS). PLS establishes security on the Physical Layer (PHY) of wireless transmissions. With new wireless technologies emerging, physical properties change. We analyze how suitable PLS techniques are in the domain of mmWave and Visible Light Communication (VLC). Despite VLC being commonly believed to be very secure due to its limited range, we show that using VLC instead for PLS is less secure than using it with Radio Frequency (RF) communication. The work in this thesis is applied to mature products as well as upcoming standards. We consider security for the whole product life cycle to make connected devices and IoT ecosystems more secure in the long term

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods

TLS on Android – Evolution over the last decade

Mobile Geräte und mobile Plattformen sind omnipräsent. Android hat sich zum bedeutendsten mobilen Betriebssystem entwickelt und bietet Milliarden Benutzer:innen eine Plattform mit Millionen von Apps. Diese bieten zunehmend Lösungen für alltägliche Probleme und sind aus dem Alltag nicht mehr wegzudenken. Mobile Apps arbeiten dazu mehr und mehr mit persönlichen sensiblen Daten, sodass ihr Datenverkehr ein attraktives Angriffsziel für Man-in-the-Middle-attacks (MitMAs) ist. Schutz gegen solche Angriffe bieten Protokolle wie Transport Layer Security (TLS) und Hypertext Transfer Protocol Secure (HTTPS), deren fehlerhafter Einsatz jedoch zu ebenso gravierenden Unsicherheiten führen kann. Zahlreiche Ereignisse und frühere Forschungsergebnisse haben diesbezüglich Schwachstellen in Android Apps gezeigt. Diese Arbeit präsentiert eine Reihe von Forschungsbeiträgen, die sich mit der Sicherheit von Android befassen. Der Hauptfokus liegt dabei auf der Netzwerksicherheit von Android Apps. Hierbei untersucht diese Arbeit verschiedene Möglichkeiten zur Verbesserung der Netzwerksicherheit und deren Erfolg, wobei sie die Situation in Android auch mit der generellen Evolution von Netzwerksicherheit in Kontext setzt. Darüber hinaus schließt diese Arbeit mit einer Erhebung der aktuellen Situation und zeigt Möglichkeiten zur weiteren Verbesserung auf.Smart devices and mobile platforms are omnipresent. Android OS has evolved to become the most dominating mobile operating system on the market with billions of devices and a platform with millions of apps. Apps increasingly offer solutions to everyday problems and have become an indispensable part of people’s daily life. Due to this, mobile apps carry and handle more and more personal and privacy-sensitive data which also involves communication with backend or third party services. Due to this, their network traffic is an attractive target for Man-in-the-Middle-attacks (MitMAs). Protection against such attacks is provided by protocols such as Transport Layer Security (TLS) and Hypertext Transfer Protocol Secure (HTTPS). Incorrect use of these, however, can impose similar vulnerabilities lead to equally serious security issues. Numerous incidents and research efforts have featured such vulnerabilities in Android apps in this regard. This thesis presents a line of research addressing security on Android with a main focus on the network security of Android apps. This work covers various approaches for improving network security on Android and investigates their efficacy as well as it puts findings in context with the general evolution of network security in a larger perspective. Finally, this work concludes with a survey of the current state of network security in Android apps and envisions directions for further improvement

ATTACKS AND COUNTERMEASURES FOR WEBVIEW ON MOBILE SYSTEMS

ABSTRACT All the mainstream mobile operating systems provide a web container, called ``WebView\u27\u27. This Web-based interface can be included as part of the mobile application to retrieve and display web contents from remote servers. WebView not only provides the same functionalities as web browser, more importantly, it enables rich interactions between mobile apps and webpages loaded inside WebView. Through its APIs, WebView enables the two-way interaction. However, the design of WebView changes the landscape of the Web, especially from the security perspective. This dissertation conducts a comprehensive and systematic study of WebView\u27s impact on web security, with a particular focus on identifying its fundamental causes. This dissertation discovers multiple attacks on WebView, and proposes new protection models to enhance the security of WebView. The design principles of these models are also described as well as the prototype implementation in Android platform. Evaluations are used to demonstrate the effectiveness and performance of these protection models

Trustworthy Wireless Personal Area Networks

In the Internet of Things (IoT), everyday objects are equipped with the ability to compute and communicate. These smart things have invaded the lives of everyday people, being constantly carried or worn on our bodies, and entering into our homes, our healthcare, and beyond. This has given rise to wireless networks of smart, connected, always-on, personal things that are constantly around us, and have unfettered access to our most personal data as well as all of the other devices that we own and encounter throughout our day. It should, therefore, come as no surprise that our personal devices and data are frequent targets of ever-present threats. Securing these devices and networks, however, is challenging. In this dissertation, we outline three critical problems in the context of Wireless Personal Area Networks (WPANs) and present our solutions to these problems. First, I present our Trusted I/O solution (BASTION-SGX) for protecting sensitive user data transferred between wirelessly connected (Bluetooth) devices. This work shows how in-transit data can be protected from privileged threats, such as a compromised OS, on commodity systems. I present insights into the Bluetooth architecture, Intel’s Software Guard Extensions (SGX), and how a Trusted I/O solution can be engineered on commodity devices equipped with SGX. Second, I present our work on AMULET and how we successfully built a wearable health hub that can run multiple health applications, provide strong security properties, and operate on a single charge for weeks or even months at a time. I present the design and evaluation of our highly efficient event-driven programming model, the design of our low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. Third, I present a new approach (VIA) that helps devices at the center of WPANs (e.g., smartphones) to verify the authenticity of interactions with other devices. This work builds on past work in anomaly detection techniques and shows how these techniques can be applied to Bluetooth network traffic. Specifically, we show how to create normality models based on fine- and course-grained insights from network traffic, which can be used to verify the authenticity of future interactions

Supporting lay users in privacy decisions when sharing sensitive data

The first part of the thesis focuses on assisting users in choosing their privacy settings, by using machine learning to derive the optimal set of privacy settings for the user. In contrast to other work, our approach uses context factors as well as individual factors to provide a personalized set of privacy settings. The second part consists of a set of intelligent user interfaces to assist the users throughout the complete privacy journey, from defining friend groups that allow targeted information sharing; through user interfaces for selecting information recipients, to find possible errors or unusual settings, and to refine them; up to mechanisms to gather in-situ feedback on privacy incidents, and investigating how to use these to improve a user’s privacy in the future. Our studies have shown that including tailoring the privacy settings significantly increases the correctness of the predicted privacy settings; whereas the user interfaces have been shown to significantly decrease the amount of unwanted disclosures.Insbesondere nach den jüngsten Datenschutzskandalen in sozialen Netzwerken wird der Datenschutz für Benutzer immer wichtiger. Obwohl die meisten Benutzer behaupten Wert auf Datenschutz zu legen, verhalten sie sich online allerdings völlig anders: Sie lassen die meisten Datenschutzeinstellungen der online genutzten Dienste, wie z. B. von sozialen Netzwerken oder Diensten zur Standortfreigabe, unberührt und passen sie nicht an ihre Datenschutzanforderungen an. In dieser Arbeit werde ich einen Ansatz zur Lösung dieses Problems vorstellen, der auf zwei verschiedenen Säulen basiert. Der erste Teil konzentriert sich darauf, Benutzer bei der Auswahl ihrer Datenschutzeinstellungen zu unterstützen, indem maschinelles Lernen verwendet wird, um die optimalen Datenschutzeinstellungen für den Benutzer abzuleiten. Im Gegensatz zu anderen Arbeiten verwendet unser Ansatz Kontextfaktoren sowie individuelle Faktoren, um personalisierte Datenschutzeinstellungen zu generieren. Der zweite Teil besteht aus einer Reihe intelligenter Benutzeroberflächen, die die Benutzer in verschiedene Datenschutzszenarien unterstützen. Dies beginnt bei einer Oberfläche zur Definition von Freundesgruppen, die im Anschluss genutzt werden können um einen gezielten Informationsaustausch zu ermöglichen, bspw. in sozialen Netzwerken; über Benutzeroberflächen um die Empfänger von privaten Daten auszuwählen oder mögliche Fehler oder ungewöhnliche Datenschutzeinstellungen zu finden und zu verfeinern; bis hin zu Mechanismen, um In-Situ- Feedback zu Datenschutzverletzungen zum Zeitpunkt ihrer Entstehung zu sammeln und zu untersuchen, wie diese verwendet werden können, um die Privatsphäreeinstellungen eines Benutzers anzupassen. Unsere Studien haben gezeigt, dass die Verwendung von individuellen Faktoren die Korrektheit der vorhergesagten Datenschutzeinstellungen erheblich erhöht. Es hat sich gezeigt, dass die Benutzeroberflächen die Anzahl der Fehler, insbesondere versehentliches Teilen von Daten, erheblich verringern

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