Web Tracking: Mechanisms, Implications, and Defenses

This articles surveys the existing literature on the methods currently used by web services to track the user online as well as their purposes, implications, and possible user's defenses. A significant majority of reviewed articles and web resources are from years 2012-2014. Privacy seems to be the Achilles' heel of today's web. Web services make continuous efforts to obtain as much information as they can about the things we search, the sites we visit, the people with who we contact, and the products we buy. Tracking is usually performed for commercial purposes. We present 5 main groups of methods used for user tracking, which are based on sessions, client storage, client cache, fingerprinting, or yet other approaches. A special focus is placed on mechanisms that use web caches, operational caches, and fingerprinting, as they are usually very rich in terms of using various creative methodologies. We also show how the users can be identified on the web and associated with their real names, e-mail addresses, phone numbers, or even street addresses. We show why tracking is being used and its possible implications for the users (price discrimination, assessing financial credibility, determining insurance coverage, government surveillance, and identity theft). For each of the tracking methods, we present possible defenses. Apart from describing the methods and tools used for keeping the personal data away from being tracked, we also present several tools that were used for research purposes - their main goal is to discover how and by which entity the users are being tracked on their desktop computers or smartphones, provide this information to the users, and visualize it in an accessible and easy to follow way. Finally, we present the currently proposed future approaches to track the user and show that they can potentially pose significant threats to the users' privacy.Comment: 29 pages, 212 reference

Betrayed by the Guardian: Security and Privacy Risks of Parental Control Solutions

For parents of young children and adolescents, the digital age has introduced many new challenges, including excessive screen time, inappropriate online content, cyber predators, and cyberbullying. To address these challenges, many parents rely on numerous parental control solutions on different platforms, including parental control network devices (e.g., WiFi routers) and software applications on mobile devices and laptops. While these parental control solutions may help digital parenting, they may also introduce serious security and privacy risks to children and parents, due to their elevated privileges and having access to a significant amount of privacy-sensitive data. In this paper, we present an experimental framework for systematically evaluating security and privacy issues in parental control software and hardware solutions. Using the developed framework, we provide the first comprehensive study of parental control tools on multiple platforms including network devices, Windows applications, Chrome extensions and Android apps. Our analysis uncovers pervasive security and privacy issues that can lead to leakage of private information, and/or allow an adversary to fully control the parental control solution, and thereby may directly aid cyberbullying and cyber predators

“And all the pieces matter...” Hybrid Testing Methods for Android App's Privacy Analysis

Smartphones have become inherent to the every day life of billions of people worldwide, and they are used to perform activities such as gaming, interacting with our peers or working. While extremely useful, smartphone apps also have drawbacks, as they can affect the security and privacy of users. Android devices hold a lot of personal data from users, including their social circles (e.g., contacts), usage patterns (e.g., app usage and visited websites) and their physical location. Like in most software products, Android apps often include third-party code (Software Development Kits or SDKs) to include functionality in the app without the need to develop it in-house. Android apps and third-party components embedded in them are often interested in accessing such data, as the online ecosystem is dominated by data-driven business models and revenue streams like advertising. The research community has developed many methods and techniques for analyzing the privacy and security risks of mobile apps, mostly relying on two techniques: static code analysis and dynamic runtime analysis. Static analysis analyzes the code and other resources of an app to detect potential app behaviors. While this makes static analysis easier to scale, it has other drawbacks such as missing app behaviors when developers obfuscate the app’s code to avoid scrutiny. Furthermore, since static analysis only shows potential app behavior, this needs to be confirmed as it can also report false positives due to dead or legacy code. Dynamic analysis analyzes the apps at runtime to provide actual evidence of their behavior. However, these techniques are harder to scale as they need to be run on an instrumented device to collect runtime data. Similarly, there is a need to stimulate the app, simulating real inputs to examine as many code-paths as possible. While there are some automatic techniques to generate synthetic inputs, they have been shown to be insufficient. In this thesis, we explore the benefits of combining static and dynamic analysis techniques to complement each other and reduce their limitations. While most previous work has often relied on using these techniques in isolation, we combine their strengths in different and novel ways that allow us to further study different privacy issues on the Android ecosystem. Namely, we demonstrate the potential of combining these complementary methods to study three inter-related issues: • A regulatory analysis of parental control apps. We use a novel methodology that relies on easy-to-scale static analysis techniques to pin-point potential privacy issues and violations of current legislation by Android apps and their embedded SDKs. We rely on the results from our static analysis to inform the way in which we manually exercise the apps, maximizing our ability to obtain real evidence of these misbehaviors. We study 46 publicly available apps and find instances of data collection and sharing without consent and insecure network transmissions containing personal data. We also see that these apps fail to properly disclose these practices in their privacy policy. • A security analysis of the unauthorized access to permission-protected data without user consent. We use a novel technique that combines the strengths of static and dynamic analysis, by first comparing the data sent by applications at runtime with the permissions granted to each app in order to find instances of potential unauthorized access to permission protected data. Once we have discovered the apps that are accessing personal data without permission, we statically analyze their code in order to discover covert- and side-channels used by apps and SDKs to circumvent the permission system. This methodology allows us to discover apps using the MAC address as a surrogate for location data, two SDKs using the external storage as a covert-channel to share unique identifiers and an app using picture metadata to gain unauthorized access to location data. • A novel SDK detection methodology that relies on obtaining signals observed both in the app’s code and static resources and during its runtime behavior. Then, we rely on a tree structure together with a confidence based system to accurately detect SDK presence without the need of any a priory knowledge and with the ability to discern whether a given SDK is part of legacy or dead code. We prove that this novel methodology can discover third-party SDKs with more accuracy than state-of-the-art tools both on a set of purpose-built ground-truth apps and on a dataset of 5k publicly available apps. With these three case studies, we are able to highlight the benefits of combining static and dynamic analysis techniques for the study of the privacy and security guarantees and risks of Android apps and third-party SDKs. The use of these techniques in isolation would not have allowed us to deeply investigate these privacy issues, as we would lack the ability to provide real evidence of potential breaches of legislation, to pin-point the specific way in which apps are leveraging cover and side channels to break Android’s permission system or we would be unable to adapt to an ever-changing ecosystem of Android third-party companies.The works presented in this thesis were partially funded within the framework of the following projects and grants: • European Union’s Horizon 2020 Innovation Action program (Grant Agreement No. 786741, SMOOTH Project and Grant Agreement No. 101021377, TRUST AWARE Project). • Spanish Government ODIO NºPID2019-111429RB-C21/PID2019-111429RBC22. • The Spanish Data Protection Agency (AEPD) • AppCensus Inc.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Srdjan Matic.- Secretario: Guillermo Suárez-Tangil.- Vocal: Ben Stoc

PALPAS - PAsswordLess PAssword Synchronization

Tools that synchronize passwords over several user devices typically store the encrypted passwords in a central online database. For encryption, a low-entropy, password-based key is used. Such a database may be subject to unauthorized access which can lead to the disclosure of all passwords by an offline brute-force attack. In this paper, we present PALPAS, a secure and user-friendly tool that synchronizes passwords between user devices without storing information about them centrally. The idea of PALPAS is to generate a password from a high entropy secret shared by all devices and a random salt value for each service. Only the salt values are stored on a server but not the secret. The salt enables the user devices to generate the same password but is statistically independent of the password. In order for PALPAS to generate passwords according to different password policies, we also present a mechanism that automatically retrieves and processes the password requirements of services. PALPAS users need to only memorize a single password and the setup of PALPAS on a further device demands only a one-time transfer of few static data.Comment: An extended abstract of this work appears in the proceedings of ARES 201

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352