Biblio

Suppose we are given a large number of sequences on a given alphabet, and an adversary is interested in identifying (de-anonymizing) a specific target sequence based on its patterns. Our goal is to thwart such an adversary by obfuscating the target sequences by applying artificial (but small) distortions to its values. A key point here is that we would like to make no assumptions about the statistical model of such sequences. This is in contrast to existing literature where assumptions (e.g., Markov chains) are made regarding such sequences to obtain privacy guarantees. We relate this problem to a set of combinatorial questions on sequence construction based on which we are able to obtain provable guarantees. This problem is relevant to important privacy applications: from fingerprinting webpages visited by users through anonymous communication systems to linking communicating parties on messaging applications to inferring activities of users of IoT devices.

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.

Decoy routing is an emerging approach for censorship circumvention in which circumvention is implemented with help from a number of volunteer Internet autonomous systems, called decoy ASes. Recent studies on decoy routing consider all decoy routing systems to be susceptible to a fundamental attack – regardless of their specific designs–in which the censors re-route traffic around decoy ASes, thereby preventing censored users from using such systems. In this paper, we propose a new architecture for decoy routing that, by design, is significantly stronger to rerouting attacks compared to all previous designs. Unlike previous designs, our new architecture operates decoy routers only on the downstream traffic of the censored users; therefore we call it downstream-only decoy routing. As we demonstrate through Internet-scale BGP simulations, downstream-only decoy routing offers significantly stronger resistance to rerouting attacks, which is intuitively because a (censoring) ISP has much less control on the downstream BGP routes of its traffic. Designing a downstream-only decoy routing system is a challenging engineering problem since decoy routers do not intercept the upstream traffic of censored users. We design the first downstream-only decoy routing system, called Waterfall, by devising unique covert communication mechanisms. We also use various techniques to make our Waterfall implementation resistant to traffic analysis attacks. We believe that downstream-only decoy routing is a significant step towards making decoy routing systems practical. This is because a downstream-only decoy routing system can be deployed using a significantly smaller number of volunteer ASes, given a target resistance to rerouting attacks. For instance, we show that a Waterfall implementation with only a single decoy AS is as resistant to routing attacks (against China) as a traditional decoy system (e.g., Telex) with 53 decoy ASes.

Decoy routing is a promising new approach for censorship circumvention that relies on traffic re-direction by volunteer autonomous systems. Decoy routing is subject to a fundamental censorship attack, called routing around decoy (RAD), in which the censors re-route their clients' Internet traffic in order to evade decoy routing autonomous systems. Recently, there has been a heated debate in the community on the real-world feasibility of decoy routing in the presence of the RAD attack. Unfortunately, previous studies rely their analysis on heuristic-based mechanisms for decoy placement strategies as well as ad hoc strategies for the implementation of the RAD attack by the censors. In this paper, we perform the first systematic analysis of decoy routing in the presence of the RAD attack. We use game theory to model the interactions between decoy router deployers and the censors in various settings. Our game-theoretic analysis finds the optimal decoy placement strategies–-as opposed to heuristic-based placements–-in the presence of RAD censors who take their optimal censorship actions–-as opposed to some ad hoc implementation of RAD. That is, we investigate the best decoy placement given the best RAD censorship. We consider two business models for the real-world deployment of decoy routers: a central deployment that resembles that of Tor and a distributed deployment where autonomous systems individually decide on decoy deployment based on their economic interests. Through extensive simulation of Internet routes, we derive the optimal strategies in the two models for various censoring countries and under different assumptions about the budget and preferences of the censors and decoy deployers. We believe that our study is a significant step forward in understanding the practicality of the decoy routing circumvention approach.