arXiv subjects

Carmela Troncoso

Publications and source records attributed to Carmela Troncoso.

At least 55 records · Page 3Linked to original sources

arXiv2019

zksk: A Library for Composable Zero-Knowledge Proofs

Zero-knowledge proofs are an essential building block in many privacy-preserving systems. However, implementing these proofs is tedious and error-prone. In this paper, we present zksk, a well-documented Python library for defining and computing sigma protocols: the most popular class of zero-knowledge proofs. In zksk, proofs compose: programmers can convert smaller proofs into building blocks that then can be combined into bigger proofs. zksk features a modern Python-based domain-specific language. This makes possible to define proofs without learning a new custom language, and to benefit from the rich Python syntax and ecosystem. The library is available at https://github.com/spring-epfl/zksk

cs.CR↗

arXiv2019

Filter Design for Delay-Based Anonymous Communications

In this work, we address the problem of designing delay-based anonymous communication systems. We consider a timed mix where an eavesdropper wants to learn the communication pattern of the users, and study how the mix must delay the messages so as to increase the adversary's estimation error. We show the connection between this problem and a MIMO system where we want to design the coloring filter that worsens the adversary's estimation of the MIMO channel matrix. We obtain theoretical solutions for the optimal filter against short-term and long-term adversaries, evaluate them with experiments, and show how some properties of filters can be used in the implementation of timed mixes. This opens the door to the application of previously known filter design techniques to anonymous communication systems.

cs.CR↗

arXiv2019

Understanding the Effects of Real-World Behavior in Statistical Disclosure Attacks

High-latency anonymous communication systems prevent passive eavesdroppers from inferring communicating partners with certainty. However, disclosure attacks allow an adversary to recover users' behavioral profiles when communications are persistent. Understanding how the system parameters affect the privacy of the users against such attacks is crucial. Earlier work in the area analyzes the performance of disclosure attacks in controlled scenarios, where a certain model about the users' behavior is assumed. In this paper, we analyze the profiling accuracy of one of the most efficient disclosure attack, the least squares disclosure attack, in realistic scenarios. We generate real traffic observations from datasets of different nature and find that the models considered in previous work do not fit this realistic behavior. We relax previous hypotheses on the behavior of the users and extend previous performance analyses, validating our results with real data and providing new insights into the parameters that affect the protection of the users in the real world.

cs.CR↗

arXiv2019

A Least Squares Approach to the Static Traffic Analysis of High-Latency Anonymous Communication Systems

Mixes, relaying routers that hide the relation between incoming and outgoing messages, are the main building block of high-latency anonymous communication networks. A number of so-called disclosure attacks have been proposed to effectively de-anonymize traffic sent through these channels. Yet, the dependence of their success on the system parameters is not well-understood. We propose the Least Squares Disclosure Attack (LSDA), in which user profiles are estimated by solving a least squares problem. We show that LSDA is not only suitable for the analysis of threshold mixes, but can be easily extended to attack pool mixes. Furthermore, contrary to previous heuristic-based attacks, our approach allows us to analytically derive expressions that characterize the profiling error of LSDA with respect to the system parameters. We empirically demonstrate that LSDA recovers users' profiles with greater accuracy than its statistical predecessors and verify that our analysis closely predicts actual performance.

cs.CR↗

arXiv2019

Meet the Family of Statistical Disclosure Attacks

Disclosure attacks aim at revealing communication patterns in anonymous communication systems, such as conversation partners or frequency. In this paper, we propose a framework to compare between the members of the statistical disclosure attack family. We compare different variants of the Statistical Disclosure Attack (SDA) in the literature, together with two new methods; as well as show their relation with the Least Squares Disclosure Attack (LSDA). We empirically explore the performance of the attacks with respect to the different parameters of the system. Our experiments show that i) our proposals considerably improve the state-of-the-art SDA and ii) confirm that LSDA outperforms the SDA family when the adversary has enough observations of the system.

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arXiv2019

Encrypted DNS --> Privacy? A Traffic Analysis Perspective

Virtually every connection to an Internet service is preceded by a DNS lookup which is performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations such as Google and Cloudflare are deploying recently standardized protocols that encrypt DNS traffic between end users and recursive resolvers such as DNS-over-TLS (DoT) and DNS-over-HTTPS (DoH). In this paper, we examine whether encrypting DNS traffic can protect users from traffic analysis-based monitoring and censoring. We propose a novel feature set to perform the attacks, as those used to attack HTTPS or Tor traffic are not suitable for DNS' characteristics. We show that traffic analysis enables the identification of domains with high accuracy in closed and open world settings, using 124 times less data than attacks on HTTPS flows. We find that factors such as location, resolver, platform, or client do mitigate the attacks performance but they are far from completely stopping them. Our results indicate that DNS-based censorship is still possible on encrypted DNS traffic. In fact, we demonstrate that the standardized padding schemes are not effective. Yet, Tor -- which does not effectively mitigate traffic analysis attacks on web traffic -- is a good defense against DoH traffic analysis.

cs.CR↗

arXiv2019

Evading classifiers in discrete domains with provable optimality guarantees

Machine-learning models for security-critical applications such as bot, malware, or spam detection, operate in constrained discrete domains. These applications would benefit from having provable guarantees against adversarial examples. The existing literature on provable adversarial robustness of models, however, exclusively focuses on robustness to gradient-based attacks in domains such as images. These attacks model the adversarial cost, e.g., amount of distortion applied to an image, as a $p$-norm. We argue that this approach is not well-suited to model adversarial costs in constrained domains where not all examples are feasible. We introduce a graphical framework that (1) generalizes existing attacks in discrete domains, (2) can accommodate complex cost functions beyond $p$-norms, including financial cost incurred when attacking a classifier, and (3) efficiently produces valid adversarial examples with guarantees of minimal adversarial cost. These guarantees directly translate into a notion of adversarial robustness that takes into account domain constraints and the adversary's capabilities. We show how our framework can be used to evaluate security by crafting adversarial examples that evade a Twitter-bot detection classifier with provably minimal number of changes; and to build privacy defenses by crafting adversarial examples that evade a privacy-invasive website-fingerprinting classifier.

cs.LG↗

arXiv2019

On (The Lack Of) Location Privacy in Crowdsourcing Applications

Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost. Specifically, mobile crowdsourcing (MCS) leverages users' devices as sensors to perform geo-located data collection. The collection of geolocated data raises serious privacy concerns for users. Yet, despite the large research body on location privacy-preserving mechanisms (LPPMs), MCS developers implement little to no protection for data collection or publication. To understand this mismatch, we study the performance of existing LPPMs on publicly available data from two mobile crowdsourcing projects. Our results show that well-established defenses are either not applicable or offer little protection in the MCS setting. Additionally, they have a much stronger impact on applications' utility than foreseen in the literature. This is because existing LPPMs, designed with location-based services (LBSs) in mind, are optimized for utility functions based on users' locations, while MCS utility functions depend on the values (e.g., measurements) associated with those locations. We finally outline possible research avenues to facilitate the development of new location privacy solutions that fit the needs of MCS so that the increasing number of such applications do not jeopardize their users' privacy.

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arXiv2019

Rethinking Location Privacy for Unknown Mobility Behaviors

Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy properties with these same data. In this paper, we aim to understand the impact of this decision on the level of privacy these LPPMs may offer in real life when the users' mobility data may be different from the data used in the design phase. Our results show that, in many cases, training data does not capture users' behavior accurately and, thus, the level of privacy provided by the LPPM is often overestimated. To address this gap between theory and practice, we propose to use blank-slate models for LPPM design. Contrary to the hardwired approach, that assumes known users' behavior, blank-slate models learn the users' behavior from the queries to the service provider. We leverage this blank-slate approach to develop a new family of LPPMs, that we call Profile Estimation-Based LPPMs. Using real data, we empirically show that our proposal outperforms optimal state-of-the-art mechanisms designed on sporadic hardwired models. On non-sporadic location privacy scenarios, our method is only better if the usage of the location privacy service is not continuous. It is our hope that eliminating the need to bootstrap the mechanisms with training data and ensuring that the mechanisms are lightweight and easy to compute help fostering the integration of location privacy protections in deployed systems.

cs.CR↗

arXiv2018

Questioning the assumptions behind fairness solutions

In addition to their benefits, optimization systems can have negative economic, moral, social, and political effects on populations as well as their environments. Frameworks like fairness have been proposed to aid service providers in addressing subsequent bias and discrimination during data collection and algorithm design. However, recent reports of neglect, unresponsiveness, and malevolence cast doubt on whether service providers can effectively implement fairness solutions. These reports invite us to revisit assumptions made about the service providers in fairness solutions. Namely, that service providers have (i) the incentives or (ii) the means to mitigate optimization externalities. Moreover, the environmental impact of these systems suggests that we need (iii) novel frameworks that consider systems other than algorithmic decision-making and recommender systems, and (iv) solutions that go beyond removing related algorithmic biases. Going forward, we propose Protective Optimization Technologies that enable optimization subjects to defend against negative consequences of optimization systems.

cs.CY↗

arXiv2018

ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging

The social demand for email end-to-end encryption is barely supported by mainstream service providers. Autocrypt is a new community-driven open specification for e-mail encryption that attempts to respond to this demand. In Autocrypt the encryption keys are attached directly to messages, and thus the encryption can be implemented by email clients without any collaboration of the providers. The decentralized nature of this in-band key distribution, however, makes it prone to man-in-the-middle attacks and can leak the social graph of users. To address this problem we introduce ClaimChain, a cryptographic construction for privacy-preserving authentication of public keys. Users store claims about their identities and keys, as well as their beliefs about others, in ClaimChains. These chains form authenticated decentralized repositories that enable users to prove the authenticity of both their keys and the keys of their contacts. ClaimChains are encrypted, and therefore protect the stored information, such as keys and contact identities, from prying eyes. At the same time, ClaimChain implements mechanisms to provide strong non-equivocation properties, discouraging malicious actors from distributing conflicting or inauthentic claims. We implemented ClaimChain and we show that it offers reasonable performance, low overhead, and authenticity guarantees.

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arXiv2018

Under the Underground: Predicting Private Interactions in Underground Forums

Underground forums where users discuss, buy, and sell illicit services and goods facilitate a better understanding of the economy and organization of cybercriminals. Prior work has shown that in particular private interactions provide a wealth of information about the cybercriminal ecosystem. Yet, those messages are seldom available to analysts, except when there is a leak. To address this problem we propose a supervised machine learning based method able to predict which public \threads will generate private messages, after a partial leak of such messages has occurred. To the best of our knowledge, we are the first to develop a solution to overcome the barrier posed by limited to no information on private activity for underground forum analysis. Additionally, we propose an automate method for labeling posts, significantly reducing the cost of our approach in the presence of real unlabeled data. This method can be tuned to focus on the likelihood of users receiving private messages, or \threads triggering private interactions. We evaluate the performance of our methods using data from three real forum leaks. Our results show that public information can indeed be used to predict private activity, although prediction models do not transfer well between forums. We also find that neither the length of the leak period nor the time between the leak and the prediction have significant impact on our technique's performance, and that NLP features dominate the prediction power.

cs.CR↗

arXiv2018

TARANET: Traffic-Analysis Resistant Anonymity at the NETwork layer

Modern low-latency anonymity systems, no matter whether constructed as an overlay or implemented at the network layer, offer limited security guarantees against traffic analysis. On the other hand, high-latency anonymity systems offer strong security guarantees at the cost of computational overhead and long delays, which are excessive for interactive applications. We propose TARANET, an anonymity system that implements protection against traffic analysis at the network layer, and limits the incurred latency and overhead. In TARANET's setup phase, traffic analysis is thwarted by mixing. In the data transmission phase, end hosts and ASes coordinate to shape traffic into constant-rate transmission using packet splitting. Our prototype implementation shows that TARANET can forward anonymous traffic at over 50~Gbps using commodity hardware.

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arXiv2017

Feature importance scores and lossless feature pruning using Banzhaf power indices

Understanding the influence of features in machine learning is crucial to interpreting models and selecting the best features for classification. In this work we propose the use of principles from coalitional game theory to reason about importance of features. In particular, we propose the use of the Banzhaf power index as a measure of influence of features on the outcome of a classifier. We show that features having Banzhaf power index of zero can be losslessly pruned without damage to classifier accuracy. Computing the power indices does not require having access to data samples. However, if samples are available, the indices can be empirically estimated. We compute Banzhaf power indices for a neural network classifier on real-life data, and compare the results with gradient-based feature saliency, and coefficients of a logistic regression model with $L_1$ regularization.

stat.ML↗

arXiv2017

Knock Knock, Who's There? Membership Inference on Aggregate Location Data

Aggregate location data is often used to support smart services and applications, e.g., generating live traffic maps or predicting visits to businesses. In this paper, we present the first study on the feasibility of membership inference attacks on aggregate location time-series. We introduce a game-based definition of the adversarial task, and cast it as a classification problem where machine learning can be used to distinguish whether or not a target user is part of the aggregates. We empirically evaluate the power of these attacks on both raw and differentially private aggregates using two mobility datasets. We find that membership inference is a serious privacy threat, and show how its effectiveness depends on the adversary's prior knowledge, the characteristics of the underlying location data, as well as the number of users and the timeframe on which aggregation is performed. Although differentially private mechanisms can indeed reduce the extent of the attacks, they also yield a significant loss in utility. Moreover, a strategic adversary mimicking the behavior of the defense mechanism can greatly limit the protection they provide. Overall, our work presents a novel methodology geared to evaluate membership inference on aggregate location data in real-world settings and can be used by providers to assess the quality of privacy protection before data release or by regulators to detect violations.

cs.CR↗

arXiv2017

Is Geo-Indistinguishability What You Are Looking for?

Since its proposal in 2013, geo-indistinguishability has been consolidated as a formal notion of location privacy, generating a rich body of literature building on this idea. A problem with most of these follow-up works is that they blindly rely on geo-indistinguishability to provide location privacy, ignoring the numerical interpretation of this privacy guarantee. In this paper, we provide an alternative formulation of geo-indistinguishability as an adversary error, and use it to show that the privacy vs.~utility trade-off that can be obtained is not as appealing as implied by the literature. We also show that although geo-indistinguishability guarantees a lower bound on the adversary's error, this comes at the cost of achieving poorer performance than other noise generation mechanisms in terms of average error, and enabling the possibility of exposing obfuscated locations that are useless from the quality of service point of view.

cs.CR↗

arXiv2017

Back to the Drawing Board: Revisiting the Design of Optimal Location Privacy-preserving Mechanisms

In the last years we have witnessed the appearance of a variety of strategies to design optimal location privacy-preserving mechanisms, in terms of maximizing the adversary's expected error with respect to the users' whereabouts. In this work, we take a closer look at the defenses created by these strategies and show that, even though they are indeed optimal in terms of adversary's correctness, not all of them offer the same protection when looking at other dimensions of privacy. To avoid "bad" choices, we argue that the search for optimal mechanisms must be guided by complementary criteria. We provide two example auxiliary metrics that help in this regard: the conditional entropy, that captures an information-theoretic aspect of the problem; and the worst-case quality loss, that ensures that the output of the mechanism always provides a minimum utility to the users. We describe a new mechanism that maximizes the conditional entropy and is optimal in terms of average adversary error, and compare its performance with previously proposed optimal mechanisms using two real datasets. Our empirical results confirm that no mechanism fares well on every privacy criteria simultaneously, making apparent the need for considering multiple privacy dimensions to have a good understanding of the privacy protection a mechanism provides.

cs.CR↗

arXiv2017

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems.

cs.CR↗