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Website fingerprinting enables an attacker to infer which web page a client is browsing through encrypted or anonymized network connections. We present a new website fingerprinting technique based on random decision forests and evaluate performance over standard web pages as well as Tor hidden services, on a larger scale than previous works. Our technique,(More)
OBJECTIVE To evaluate the effectiveness and costs of a multifaceted flexible educational programme aimed at reducing antibiotic dispensing at the practice level in primary care. DESIGN Randomised controlled trial with general practices as the unit of randomisation and analysis. Clinicians and researchers were blinded to group allocation until after(More)
Website Fingerprinting (WF) allows a passive network adversary to learn the websites that a client visits by analyzing traffic patterns that are unique to each website. It has been recently shown that these attacks are particularly effective against .onion sites, anonymous web servers hosted within the Tor network. Given the sensitive nature of the content(More)
“Entry” guards protect the Tor onion routing system from variants of the “predecessor” attack, that would allow an adversary with control of a fraction of routers to eventually de-anonymize some users. Research has however shown the three guard scheme has drawbacks and Dingledine et al. proposed in 2014 for each user to have a single long-term guard. We(More)
AnoNotify is a service for private, timely and low-cost online notifications. We present the design and security arguments behind AnoNotify, as well as an evaluation of its cost. AnoNotify is based on mixnetworks, Bloom filters and shards. We present a security definition and security proofs for AnoNotify. We then discuss a number of applications, including(More)
Recent advances in machine learning are paving the way for the artificial generation of high quality images and videos. In this paper, we investigate how generating synthetic samples through generative models can lead to information leakage, and, consequently, to privacy breaches affecting individuals’ privacy that contribute their personal or sensitive(More)
We present Loopix, a low-latency anonymous communication system that provides bi-directional ‘third-party’ sender and receiver anonymity and unobservability. Loopix leverages cover traffic and Poisson mixing—brief independent message delays—to provide anonymity and to achieve traffic analysis resistance against, including but not limited to, a global(More)
Adversarial training has proved to be competitive against supervised learning methods on computer vision tasks. However, studies have mainly been confined to generative tasks such as image synthesis. In this paper, we apply adversarial training techniques to the discriminative task of learning a steganographic algorithm. Steganography is a collection of(More)
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. Until now, black-box attacks against neural networks have relied on transferability of adversarial examples. White-box attacks are used to generate adversarial(More)