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Dive into the research topics where Jamie Hayes is active.

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Featured researches published by Jamie Hayes.


privacy enhancing technologies | 2017

Website Fingerprinting Defenses at the Application Layer

Giovanni Cherubin; Jamie Hayes; Marc Juarez

Abstract 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 of these services, the implications of WF on the Tor network are alarming. Prior work has only considered defenses at the client-side arguing that web servers lack of incentives to adopt countermeasures. Furthermore, most of these defenses have been designed to operate on the stream of network packets, making practical deployment difficult. In this paper, we propose two application-level defenses including the first server-side defense against WF, as .onion services have incentives to support it. The other defense is a lightweight client-side defense implemented as a browser add-on, improving ease of deployment over previous approaches. In our evaluations, the server-side defense is able to reduce WF accuracy on Tor .onion sites from 69.6% to 10% and the client-side defense reduces accuracy from 64% to 31.5%.


workshop on privacy in the electronic society | 2017

AnNotify: A Private Notification Service

Ania M. Piotrowska; Jamie Hayes; Nethanel Gelernter; George Danezis; Amir Herzberg

AnNotify is a scalable service for private, timely and low-cost online notifications, based on anonymous communication, sharding, dummy queries, and Bloom filters. We present the design and analysis of AnNotify, as well as an evaluation of its costs. We outline the design of AnNotify and calculate the concrete advantage of an adversary observing multiple queries. We present a number of extensions, such as generic presence and broadcast notifications, and applications, including notifications for incoming messages in anonymous communications, updates to private cached web and Domain Name Service (DNS) queries.


privacy enhancing technologies | 2015

Guard Sets for Onion Routing

Jamie Hayes; George Danezis

Abstract “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 first show that such a guard selection strategy would be optimal if the Tor network was failure-free and static. However under realistic failure conditions the one guard proposal still suffers from the classic fingerprinting attacks, uniquely identifying users. Furthermore, under dynamic network conditions using single guards offer smaller anonymity sets to users of fresh guards. We propose and analyze an alternative guard selection scheme by way of grouping guards together to form shared guard sets. We compare the security and performance of guard sets with the three guard scheme and the one guard proposal. We show guard sets do provide increased resistance to a number of attacks, while foreseeing no significant degradation in performance or bandwidth utilization.


usenix security symposium | 2016

k-fingerprinting: A Robust Scalable Website Fingerprinting Technique

Jamie Hayes; George Danezis


Archive | 2017

Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples.

Jamie Hayes; George Danezis


usenix security symposium | 2017

The Loopix Anonymity System

Ania M. Piotrowska; Jamie Hayes; Tariq Elahi; Sebastian Meiser; George Danezis


neural information processing systems | 2017

Generating steganographic images via adversarial training

Jamie Hayes; George Danezis


arXiv: Cryptography and Security | 2017

LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks.

Jamie Hayes; Luca Melis; George Danezis; Emiliano De Cristofaro


ieee symposium on security and privacy | 2018

Learning Universal Adversarial Perturbations with Generative Models

Jamie Hayes; George Danezis


workshop on privacy in the electronic society | 2016

TASP: Towards Anonymity Sets that Persist

Jamie Hayes; Carmela Troncoso; George Danezis

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George Danezis

University College London

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Luca Melis

University College London

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Carmela Troncoso

Katholieke Universiteit Leuven

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Nethanel Gelernter

College of Management Academic Studies

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Amir Herzberg

University of Connecticut

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Tariq Elahi

University of Waterloo

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Amir Herzberg

University of Connecticut

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