A sensitive data access model in support of learning health systems

Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR

Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning

Embedding covert streams into a cover channel is a common approach to circumventing Internet censorship, due to censors' inability to examine encrypted information in otherwise permitted protocols (Skype, HTTPS, etc.). However, recent advances in machine learning (ML) enable detecting a range of anti-censorship systems by learning distinct statistical patterns hidden in traffic flows. Therefore, designing obfuscation solutions able to generate traffic that is statistically similar to innocuous network activity, in order to deceive ML-based classifiers at line speed, is difficult. In this paper, we formulate a practical adversarial attack strategy against flow classifiers as a method for circumventing censorship. Specifically, we cast the problem of finding adversarial flows that will be misclassified as a sequence generation task, which we solve with Amoeba, a novel reinforcement learning algorithm that we design. Amoeba works by interacting with censoring classifiers without any knowledge of their model structure, but by crafting packets and observing the classifiers' decisions, in order to guide the sequence generation process. Our experiments using data collected from two popular anti-censorship systems demonstrate that Amoeba can effectively shape adversarial flows that have on average 94% attack success rate against a range of ML algorithms. In addition, we show that these adversarial flows are robust in different network environments and possess transferability across various ML models, meaning that once trained against one, our agent can subvert other censoring classifiers without retraining

Meteor: Cryptographically Secure Steganography for Realistic Distributions

Despite a long history of research and wide-spread applications to censorship resistant systems, practical steganographic systems capable of embedding messages into realistic communication distributions, like text, do not exist. We identify two primary impediments to deploying universal steganography: (1) prior work leaves the difficult problem of finding samplers for non-trivial distributions unaddressed, and (2) prior constructions have impractical minimum entropy requirements. We investigate using generative models as steganographic samplers, as they represent the best known technique for approximating human communication. Additionally, we study methods to overcome the entropy requirement, including evaluating existing techniques and designing a new steganographic protocol, called Meteor. The resulting protocols are provably indistinguishable from honest model output and represent an important step towards practical steganographic communication for mundane communication channels. We implement Meteor and evaluate it on multiple computation environments with multiple generative models

Practical Countermeasures Against Network Censorship

Governments around the world threaten free communication on the Internet by building increasingly complex systems to carry out Network Censorship. Network Censorship undermines citizens' ability to access websites and services of their preference, damages freedom of the press and self-expression, and threatens public safety, motivating the development of censorship circumvention tools. Inevitably, censors respond by detecting and blocking those tools, using a wide range of techniques including Enumeration Attacks, Deep Packet Inspection, Traffic Fingerprinting, and Active Probing. In this dissertation, I study some of the most common attacks, actually adopted by censors in practice, and propose novel attacks to assist in the development of defenses against them. I describe practical countermeasures against those attacks, which often rely on empiric measurements of real-world data to maximize their efficiency. This dissertation also reports how this work has been successfully deployed to several popular censorship circumvention tools to help censored Internet users break free of the repressive information control.</p

Double-edged Sword: An in-depth analysis of browser fingerprints for web tracking and bot defenses

The use of browser fingerprints is a double-edged sword. While they can protect users' security by defending against bot and fraud attacks, they can also be used for web tracking, which violates individual users' privacy. While acknowledging that the use of browser fingerprints is a double-edged sword, my work is to achieve a balance that maximizes the benefits and minimizes the risks. In the following section, I provide a comprehensive examination of browser fingerprints and demonstrates how they can be improved to better protect users’ privacy while still providing a valuable tool in defending against bot and fraud attacks

Améliorer la sécurité et la vie privée sur le Web à travers les empreintes de navigateur

I have been an associate professor in computer science at the University of Lille and a member of the Spirals project-team in the CRIStAL laboratory since September 2014. I obtained my PhD in Software Engineering in Grenoble in 2013, focusing on building robust self-adaptive component-based systems, and I completed a postdoctoral stay in the Inria DiverSE project-team, in Rennes, in the area of component-based software engineering. Since 2014, my research has mostly focused on (i) multi-cloud computing and (ii) security and privacy on the web. I have successfully co-advised two doctorates, Gustavo Sousa (defended July 2018) and Antoine Vastel (defended November 2019), and currently advise 3 students. I have decided to write my Habilitation pour Diriger des Recherches (HDR) in the area of privacy and security because this will be my main line of research activities for the near future. More specifically, I present the results of the research that my students, colleagues, collaborators, and I have done in the area of browser fingerprinting.Browser fingerprinting is the process of identifying devices by accessing a collection of relatively stable attributes through Web browsers. We call the generated identifiers browser fingerprints. Fingerprints are stateless identifiers and no information is stored on the client’s device. In the first half of this manuscript, we identify and study three properties of browser fingerprinting that make it both a risk to privacy, but also of use for security. The first property, uniqueness, is the power to uniquely identify a device. We performed a large scale study on fingerprint uniqueness and, although not a perfect identifier, we show its statistical qualities allow uniquely identifying a high percentage of both desktops and mobile devices [Laperdrix 2016]. The second property, linkability, is the capacity to re-identify, or link, fingerprints over time. This is arguably the main risk to privacy and enables fingerprint tracking. We show, through two algorithms, that some devices are highly trackable, while other devices’ fingerprints are too similar to be tracked over time [Vastel 2018b]. The third and final property is consistency, which refers to the capacity to verify the attributes in a fingerprint. Through redundancies, correlations or dependencies, many attributes are verifiable, making them more difficult to spoof convincingly. We show that most countermeasures to browser fingerprinting are identifiable through such inconsistencies [Vastel 2018a], a useful property for security applications.In the second half of this manuscript, we look at the same properties from a different angle. We create a solution that breaks fingerprint linkability by randomly generating usable browsing platforms that are unique and consistent [Laperdrix 2015]. We also propose an automated testing framework to provide feedback to the developers of browsers and browser extensions to assist them in reducing the uniqueness or their products [Vastel 2018c]. Finally, we look at how fingerprint consistency is exploited in-the-wild to protect websites against automated Web crawlers. We show that fingerprinting is effective and fast to block crawlers, but lacks resiliency when facing a determined adversary [Vastel 2020].Beyond the results I report in this manuscript, I draw perspectives for exploring browser fingerprinting for multi-factor authentication, with a planned large-scale deployment in the following months. I also believe there is potential in automated testing to improve privacy. And of course, we know that fingerprint tracking does not happen in a bubble, it is complementary to other techniques. I am therefore exploring other tracking techniques, such as our preliminary results around IP addresses [Mishra 2020] and caches [Mishra 2021], using ad blockers against their users, and a few other ideas to improve privacy and security on the Web.Les empreintes de navigateurs (en anglais browser fingerprinting) sont un mécanisme qui permet d’identifier les navigateurs Internet au travers de leurs caractéristiques et configurations uniques. Nous avons identifié trois propriétés des empreintes de navigateurs qui posent un risque pour la vie privée mais qui rendent possible des utilisations en sécurité. Ces propriétés sont l’unicité, qui permet de discriminer un navigateur parmi d’autres, la liaison d’empreintes, qui permet de suivre dans le temps un dispositif, et la cohérence, qui permet de vérifier une empreinte et rend difficile les contre-mesures. Dans la première moitié de ce manuscrit, nous explorons les qualités statistiques des empreintes de navigateurs, ainsi que la possibilité et l’efficacité de les tracer dans le temps, et nous concluons sur les propriétés statistiques imparfaites mais tout de même utiles de cet indicateur. Nous montrons également que les contre-mesures pour se protéger sont défaillantes et parfois même contre-productives.Dans la seconde partie de ce manuscrit, nous regardons les défenses et utilisations des empreintes de navigateur. Nous proposons un outil pour casser la liaison d’empreintes sans l’introduction d’incohérences, limitant ainsi le traçage. Nous avons également proposé un cadre de test automatisé pour réduire l’identifiabilité des navigateurs et de leurs extensions. Finalement, nous avons étudié comment l’analyse de cohérence des empreintes est utilisée sur le Web pour bloquer des robots, et nous concluons que cette technique est rapide mais manque encore de résilience, dont l’efficacité mériterait d’être améliorée contre des attaquants déterminés.Au-delà des résultats présentés dans ce manuscrit, je présente également des perspectives pour les recherches dans ce domaine particulièrement dynamique, avec notamment l’utilisation des empreintes de navigateur pour l’authentification multi-facteurs et l’utilisation des tests automatiques pour améliorer la vie privée des usagers. Nos résultats préliminaires sur l’utilisation d’adresses IP pour le traçage, les caches de navigateur, et les bloqueurs de publicité, méritent également d’être approfondis afin de continuer à renforcer la vie privée et la sécurité sur le Web

Security and Privacy for the Modern World

The world is organized around technology that does not respect its users. As a precondition of participation in digital life, users cede control of their data to third-parties with murky motivations, and cannot ensure this control is not mishandled or abused. In this work, we create secure, privacy-respecting computing for the average user by giving them the tools to guarantee their data is shielded from prying eyes. We first uncover the side channels present when outsourcing scientific computation to the cloud, and address them by building a data-oblivious virtual environment capable of efficiently handling these workloads. Then, we explore stronger privacy protections for interpersonal communication through practical steganography, using it to hide sensitive messages in realistic cover distributions like English text. Finally, we discuss at-home cryptography, and leverage it to bind a user’s access to their online services and important files to a secure location, such as their smart home. This line of research represents a new model of digital life, one that is both full-featured and protected against the security and privacy threats of the modern world

Last-Mile TLS Interception: Analysis and Observation of the Non-Public HTTPS Ecosystem

Transport Layer Security (TLS) is one of the most widely deployed cryptographic protocols on the Internet that provides confidentiality, integrity, and a certain degree of authenticity of the communications between clients and servers. Following Snowden's revelations on US surveillance programs, the adoption of TLS has steadily increased. However, encrypted traffic prevents legitimate inspection. Therefore, security solutions such as personal antiviruses and enterprise firewalls may intercept encrypted connections in search for malicious or unauthorized content. Therefore, the end-to-end property of TLS is broken by these TLS proxies (a.k.a. middleboxes) for arguably laudable reasons; yet, may pose a security risk. While TLS clients and servers have been analyzed to some extent, such proxies have remained unexplored until recently. We propose a framework for analyzing client-end TLS proxies, and apply it to 14 consumer antivirus and parental control applications as they break end-to-end TLS connections. Overall, the security of TLS connections was systematically worsened compared to the guarantees provided by modern browsers. Next, we aim at exploring the non-public HTTPS ecosystem, composed of locally-trusted proxy-issued certificates, from the user's perspective and from several countries in residential and enterprise settings. We focus our analysis on the long tail of interception events. We characterize the customers of network appliances, ranging from small/medium businesses and institutes to hospitals, hotels, resorts, insurance companies, and government agencies. We also discover regional cases of traffic interception malware/adware that mostly rely on the same Software Development Kit (i.e., NetFilter). Our scanning and analysis techniques allow us to identify more middleboxes and intercepting apps than previously found from privileged server vantages looking at billions of connections. We further perform a longitudinal study over six years of the evolution of a prominent traffic-intercepting adware found in our dataset: Wajam. We expose the TLS interception techniques it has used and the weaknesses it has introduced on hundreds of millions of user devices. This study also (re)opens the neglected problem of privacy-invasive adware, by showing how adware evolves sometimes stronger than even advanced malware and poses significant detection and reverse-engineering challenges. Overall, whether beneficial or not, TLS interception often has detrimental impacts on security without the end-user being alerted