From a Trickle to a Flood: Active Attacks on Several Mix Types

Serjantov, Andrei; Dingledine, Roger; Syverson, Paul

doi:10.1007/3-540-36415-3_3

Cited by 120 publications

(119 citation statements)

References 5 publications

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“…Thus, in order to achieve any meaningful anonymity guarantee, we need that f < n − 1. In other words, we do not consider the n − 1 attack [55] in this work. Finally, for termination, we assume that the bulletin board is honest.…”

Section: Threat Modelmentioning

confidence: 99%

PathShuffle: Credit Mixing and Anonymous Payments for Ripple

Moreno-Sanchez

Ruffing

Kate

2017

Proceedings on Privacy Enhancing Technologies

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Abstract:The I owe you (IOU) credit network Ripple is one of the most prominent alternatives in the burgeoning field of decentralized payment systems. Ripple's path-based transactions set it apart from cryptocurrencies such as Bitcoin. Its pseudonymous nature, while still maintaining some regulatory capabilities, has motivated several financial institutions across the world to use Ripple for processing their daily transactions. Nevertheless, with its public ledger, a credit network such as Ripple is no different from a cryptocurrency in terms of weak privacy; recent demonstrative deanonymization attacks raise important concerns regarding the privacy of the Ripple users and their transactions. However, unlike for cryptocurrencies, there is no known privacy solution compatible with the existing credit networks such as Ripple. In this paper, we present PathShuffle, the first path mixing protocol for credit networks. PathShuffle is fully compatible with the current credit networks. As its essential building block, we propose PathJoin, a novel protocol to perform atomic transactions in credit networks. Using PathJoin and the P2P mixing protocol DiceMix, PathShuffle is a decentralized solution for anonymizing path-based transactions. We demonstrate the practicality of PathShuffle by performing path mixing in Ripple.

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Section: Threat Modelmentioning

confidence: 99%

PathShuffle: Credit Mixing and Anonymous Payments for Ripple

Moreno-Sanchez

Ruffing

Kate

2017

Proceedings on Privacy Enhancing Technologies

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“…A substantial part of this literature consists of papers evaluating the effectiveness of mix-based anonymity systems in a theoretical setting; e.g., [6,11,18]. Such work often involves assumptions suchas"users pick their communication partners uniformly at random" which help with the mathematics of calculating anonymity, and hence aid our understanding and intuition, but do not necessarily hold in practice.…”

Section: Related Workmentioning

confidence: 99%

On the Impact of Social Network Profiling on Anonymity

Díaz

Troncoso

Serjantov³

2008

Privacy Enhancing Technologies

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Abstract. This paper studies anonymity in a setting where individuals who communicate with each other over an anonymous channel are also members of a social network. In this setting the social network graph is known to the attacker. We propose a Bayesian method to combine multiple available sources of information and obtain an overall measure of anonymity. We study the effects of network size and find that in this case anonymity degrades when the network grows. We also consider adversaries with incomplete or erroneous information; characterize their knowledge of the social network by its quantity, quality and depth; and discuss the implications of these properties for anonymity.

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“…For example, a delay-and-playout technique (traffic mixing) is used in [2] [14]. Serjantov et al [17] classify traffic mixing strategies into two major categories-simple MIXes and pool MIXes. Both categories use either one threshold or both: a message pool size n and/or a time period t that a message stays in the pool.…”

Section: B Related Workmentioning

confidence: 99%

“…These threshold based schemes are vulnerable to either flooding attacks, where the adversary sends its own n-1 messages to flush a pool of size n; or trickle attacks, where the adversary blocks all the incoming messages except the target one until the mix node sends it out after t; or the two blended. Various mixing schemes have been proposed to reduce the success ratio of such attacks [17].…”

Section: B Related Workmentioning

confidence: 99%

Effective Probabilistic Approach Protecting Sensor Traffic

Hong

Wang²,

Kong³

et al.

MILCOM 2005 - 2005 IEEE Military Communications Conference

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Abstract-Sensor networks are often deployed in environments where malicious nodes present. Among all possible forms of the attacks threatening the sensor networks, in this work, we focus on traffic analysis attacks. Typically, in performing traffic analysis, an attacker will eavesdrop on-going wireless transmissions and analyze contents and timing instances of the transmissions so to infer critical events or to trace valuable assets in the network (e.g, data sources or sinks). The paper presents a probabilistic approach to shape the sensor network traffic to decorrelate time instances in transmissions. The security properties of the approach are studied both analytically and empirically, showing strong protection in high probability.

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From a Trickle to a Flood: Active Attacks on Several Mix Types

Cited by 120 publications

References 5 publications

PathShuffle: Credit Mixing and Anonymous Payments for Ripple

PathShuffle: Credit Mixing and Anonymous Payments for Ripple

On the Impact of Social Network Profiling on Anonymity

Effective Probabilistic Approach Protecting Sensor Traffic

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