Statistical Disclosure or Intersection Attacks on Anonymity Systems

Danezis, George; Serjantov, Andrei

doi:10.1007/978-3-540-30114-1_21

Cited by 111 publications

(102 citation statements)

References 11 publications

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…7 We simulate a population of N users users with f contacts each, to whom they send messages with equal probability (i.e., p j,i = 1/f if i is friends with j, zero otherwise). In order to easily study the influence of the system parameters on the success of the attack, in our simulations we further fix the senders that send messages to each receiver to be f j = f .…”

Section: Methodsmentioning

confidence: 99%

“…The attack estimates user profiles by solving a Least Squares problem, ensuring that the mean squared error between the real and estimated profiles is minimized. We empirically show that our attack indeed minimizes the mean squared error with respect to heuristic disclosure attack variants [3,7,21], although it performs slightly worse than the Bayesian approach [8] in the scenarios considered in this paper.…”

Section: Introductionmentioning

confidence: 90%

“…Danezis considers that in each round where Alice sends a message, the recipient anonymity set of this message is uniform over the receivers present in the round (and zero for the rest of users). The SDA was subsequently extended to more complex mixing algorithms [7], to traffic containing replies [5], to consider other users in order to improve the identification of Alice's contacts [14], and to evaluate more complex user models [15].…”

Section: Related Workmentioning

confidence: 99%

“…A variety of Disclosure or Intersection Attacks [1,3,7,8,11,12,18,19,21] have been proposed to uncover persistent and repeated patterns of communication taking place through a mix. In a nutshell, these attacks find a target user's likely set of friends, also known as user profile, by intersecting the recipient anonymity sets of the messages this user sends.…”

Section: Introductionmentioning

confidence: 99%

“…Statistical variants [3,7,21] rely on heuristics to operate, while the Bayesian inference-based method by Danezis and Troncoso [8] use Bayesian sampling techniques to recover accurately users' profiles in more complex systems than its predecessors. For any of these attacks it is difficult to obtain analytic results that characterize the dependence of their effectiveness on the parameters of the system, and hence they (as well as their sequels [5,14,15,18]) have been mostly evaluated through simulation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Understanding Statistical Disclosure: A Least Squares Approach

Pérez-González

Troncoso

2012

Privacy Enhancing Technologies

View full text Add to dashboard Cite

Abstract. It is widely accepted that Disclosure Attacks are effective against high-latency anonymous communication systems. A number of Disclosure Attack variants can be found in the literature that effectively de-anonymize traffic sent through a threshold mix. Nevertheless, these attacks' performance has been mostly evaluated through simulation and how their effectiveness varies with the parameters of the system is not well-understood. We present the LSDA, a novel disclosure attack based on the Maximum Likelihood (ML) approach, in which user profiles are estimated solving a Least Squares problem. Further, contrary to previous heuristic-based attacks, our approach allows to analytically derive formulae that characterize the profiling error of the LSDA with respect to the system's parameters. We verify through simulation that our predictors for the error closely model reality, and that the LSDA recovers users' profiles with greater accuracy than its predecessors.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Understanding Statistical Disclosure: A Least Squares Approach

Pérez-González

Troncoso

2012

Privacy Enhancing Technologies

View full text Add to dashboard Cite

show abstract

Anti‐virus in‐the‐cloud service: are we ready for the security evolution?

Yan¹,

Ansari

2011

Security Comm Networks

View full text Add to dashboard Cite

The ever‐increasing malware variants pose serious challenges for traditional signature‐based anti‐virus (AV) scan engines. To effectively handle the scale and magnitude of new malware variants, AV functionality is being moved from the user desktop into the cloud. AV in‐the‐cloud service is becoming the next‐generation security infrastructure designed to defend against virus threats. It provides reliable protection service delivered through data centers worldwide, which are built on virtualization technologies. Nowadays, cloud‐based security services are gaining bullish projections in both consumer and enterprise markets. However, are we getting ready for the cloud evolution? Security vendors are facing various challenges regarding the architectural design, implementation, and validation. Owing to the lack of operation standards among vendors and very few research works conducted up to this point, researchers have no references of AV cloud testing to rely on. In this paper, the architecture of AV in‐the‐cloud service is described. The challenges and solutions are discussed and illustrated by examples taken from our cutting‐edge research on practical applications. Copyright © 2011 John Wiley & Sons, Ltd.

show abstract