https://arxiv.org/abs/1808.09237 We model and analyze passive adversaries that monitors Tor traffic crossing the border of a jurisdiction an adversary is controlling. We show that a single adversary is able to connect incoming and outgoing traffic of their border, tracking the traffic, and cooperating adversaries are able to reconstruct parts of the Tor network, revealing user-server relationships. In our analysis we created two algorithms to estimate the capabilities of the adversaries. The first generates Tor-like traffic and the second analyzes and reconstructs the simulated data. https://arxiv.org/pdf/1808.07285 Flow correlation is the core technique used in a multitude of deanonymization attacks on Tor. Despite the importance of flow correlation attacks on Tor, existing flow correlation techniques are considered to be ineffective and unreliable in linking Tor flows when applied at a large scale, i.e., they impose high rates of false positive error rates or require impractically long flow observations to be able to make reliable correlations. In this paper, we show that, unfortunately, flow correlation attacks can be conducted on Tor traffic with drastically higher accuracies than before by leveraging emerging learning mechanisms. We particularly design a system, called DeepCorr, that outperforms the state-of-the-art by signifi- cant margins in correlating Tor connections. DeepCorr leverages an advanced deep learning architecture to learn a flow correlation function tailored to Tor's complex network--this is in contrast to previous works' use of generic statistical correlation metrics to cor- related Tor flows. We show that with moderate learning, DeepCorr can correlate Tor connections (and therefore break its anonymity) with accuracies significantly higher than existing algorithms, and using substantially shorter lengths of flow observations. For in- stance, by collecting only about 900 packets of each target Tor flow (roughly 900KB of Tor data), DeepCorr provides a flow correlation accuracy of 96% compared to 4% by the state-of-the-art system of RAPTOR using the same exact setting. We hope that our work demonstrates the escalating threat of flow correlation attacks on Tor given recent advances in learning algorithms, calling for the timely deployment of effective counter- measures by the Tor community. https://arxiv.org/pdf/1901.04434 In this work we show that Tor is vulnerable to app deanonymization attacks on Android devices through network traffic analysis. For this purpose, we describe a general methodology for performing an attack that allows to deanonymize the apps running on a target smartphone using Tor, which is the victim of the attack. Then, we discuss a Proof-of-Concept, implementing the methodology, that shows how the attack can be performed in practice and allows to assess the deanonymization accuracy that it is possible to achieve. While attacks against Tor anonymity have been already gained considerable attention in the context of website fingerprinting in desktop environments, to the best of our knowledge this is the first work that highlights Tor vulnerability to apps deanonymization attacks on Android devices. In our experiments we achieved an accuracy of 97%