arXiv subjects

Tariq Elahi

Publications and source records attributed to Tariq Elahi.

8 recordsLinked to original sources

arXiv2026

SaTor: Exploring Satellite Routing in Tor to Reduce Latency

High latency is a critical limitation within the Tor network that has a negative impact on web application responsiveness. A key factor exacerbating Tor latency is the creation of lengthy circuits that span across geographically distant regions, causing significant transmission delays. A common solution involves modifying Tor's circuit-building process to reduce the likelihood of selecting lengthy circuits. However, this strategy compromises Tor's routing randomness, increasing the risk of deanonymization. Reducing Tor's latency while minimizing security degradation presents a challenge. This paper proposes and investigates SaTor, a satellite-assisted routing scheme to reduce Tor latency. By equipping Tor relays with satellite network access, SaTor could accelerate slow circuits via satellite transmission, without biasing the existing path selection process. Our performance evaluation, using a simulator we developed along with real-world measurements, shows that over the long term, SaTor provides an expected speed-up of 21.8 ms for over 40% of circuits, with only 100 top relays equipped with satellite service. Our research uncovers a viable way to overcome Tor's latency bottleneck, serving as a practical reference for its future enhancement.

cs.CR↗

arXiv2025

Quantifying Mix Network Privacy Erosion with Generative Models

Modern mix networks improve over Tor and provide stronger privacy guarantees by robustly obfuscating metadata. As long as a message is routed through at least one honest mixnode, the privacy of the users involved is safeguarded. However, the complexity of the mixing mechanisms makes it difficult to estimate the cumulative privacy erosion occurring over time. This work uses a generative model trained on mixnet traffic to estimate the loss of privacy when users communicate persistently over a period of time. We train our large-language model from scratch on our specialized network traffic ``language'' and then use it to measure the sender-message unlinkability in various settings (e.g. mixing strategies, security parameters, observation window). Our findings reveal notable differences in privacy levels among mix strategies, even when they have similar mean latencies. In comparison, we demonstrate the limitations of traditional privacy metrics, such as entropy and log-likelihood, in fully capturing an adversary's potential to synthesize information from multiple observations. Finally, we show that larger models exhibit greater sample efficiency and superior capabilities implying that further advancements in transformers will consequently enhance the accuracy of model-based privacy estimates.

cs.CR↗

arXiv2024

Towards Flexible Anonymous Networks

Anonymous Communication designs such as Tor build their security on distributed trust over many volunteers running relays in diverse global locations. In practice, this distribution leads to a heterogeneous network in which many versions of the Tor software co-exist, each with differing sets of protocol features. Because of this heterogeneity, Tor developers employ forward-compatible protocol design as a strategy to maintain network extensibility. This strategy aims to guarantee that different versions of the Tor software interact without unrecoverable errors. In this work, we cast protocol tolerance that is enabled by forward-compatible protocol considerations as a fundamental security issue. We argue that, while being beneficial for the developers, protocol tolerance has resulted in a number of strong attacks against Tor in the past fifteen years. To address this issue, we propose Flexible Anonymous Network (FAN), a new software architecture for volunteer-based distributed networks that shifts the dependence away from protocol tolerance without losing the ability for developers to ensure the continuous evolution of their software. We i) instantiate an implementation, ii) evaluate its overheads and, iii) experiment with several of FAN's benefits to defend against a severe attack still applicable to Tor today.

cs.CR↗

arXiv2023

Accelerated Shapley Value Approximation for Data Evaluation

Data valuation has found various applications in machine learning, such as data filtering, efficient learning and incentives for data sharing. The most popular current approach to data valuation is the Shapley value. While popular for its various applications, Shapley value is computationally expensive even to approximate, as it requires repeated iterations of training models on different subsets of data. In this paper we show that the Shapley value of data points can be approximated more efficiently by leveraging the structural properties of machine learning problems. We derive convergence guarantees on the accuracy of the approximate Shapley value for different learning settings including Stochastic Gradient Descent with convex and non-convex loss functions. Our analysis suggests that in fact models trained on small subsets are more important in the context of data valuation. Based on this idea, we describe $δ$-Shapley -- a strategy of only using small subsets for the approximation. Experiments show that this approach preserves approximate value and rank of data, while achieving speedup of up to 9.9x. In pre-trained networks the approach is found to bring more efficiency in terms of accurate evaluation using small subsets.

cs.LG↗

arXiv2023

VeraSel: Verifiable Random Selection for Mixnets Construction

The security and performance of Mixnets depends on the trustworthiness of the Mixnodes in the network. The challenge is to limit the adversary's influence on which Mixnodes operate in the network. A trusted party (such as the Mixnet operator) may ensure this, however, it is a single point of failure in the event of corruption or coercion. Therefore, we study the problem of how to select a subset of Mixnodes in a distributed way for Mixnet construction. We present VeraSel, a scheme that enables Mixnodes to be chosen according to their weights in a distributed, unbiased, and verifiable fashion using Verifiable Random Functions (VRFs). It is shown that VeraSel enables any party to learn and verify which nodes has been selected based on the commitments and proofs generated by each Mixnode with VRF.

cs.CR↗

arXiv2022

Stopping Silent Sneaks: Defending against Malicious Mixes with Topological Engineering

Mixnets provide strong meta-data privacy and recent academic research and industrial projects have made strides in making them more secure, performance, and scalable. In this paper, we focus our work on stratified Mixnets -- a popular design with real-world adoption -- and identify that there still exist heretofore inadequately explored practical aspects such as: relay sampling and topology placement, network churn, and risks due to real-world usage patterns. We show that, due to the lack of incorporating these aspects, Mixnets of this type are far more susceptible to user deanonymization than expected. In order to reason and resolve these issues, we model Mixnets as a three-stage ``Sample-Placement-Forward'' pipeline, and using the results of our evaluation propose a novel Mixnet design, Bow-Tie. Bow-Tie mitigates user deanonymization through a novel adaption of Tor's guard design with an engineered guard layer and client guard-logic for stratified mixnets. We show that Bow-Tie has significantly higher user anonymity in the dynamic setting, where the Mixnet is used over a period of time, and is no worse in the static setting, where the user only sends a single message. We show the necessity of both the guard layer and client guard-logic in tandem as well as their individual effect when incorporated into other reference designs. Ultimately, Bow-Tie is a significant step towards addressing the gap between the design of Mixnets and practical deployment and wider adoption because it directly addresses real-world user and Mixnet operator concerns.

cs.CR↗

arXiv2017

The Loopix Anonymity System

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 brief message delays to provide anonymity and achieve traffic analysis resistance, including against a global network adversary. Mixes and clients self-monitor the network via loops of traffic to provide protection against active attacks, and inject cover traffic to provide stronger anonymity and a measure of sender and receiver unobservability. Service providers mediate access in and out of a stratified network of Poisson mix nodes to facilitate accounting and off-line message reception, as well as to keep the number of links in the system low, and to concentrate cover traffic. We provide a theoretical analysis of the Poisson mixing strategy as well as an empirical evaluation of the anonymity provided by the protocol and a functional implementation that we analyze in terms of scalability by running it on AWS EC2. We show that a Loopix relay can handle upwards of 300 messages per second, at a small delay overhead of less than 1.5 ms on top of the delays introduced into messages to provide security. Overall message latency is in the order of seconds - which is low for a mix-system. Furthermore, many mix nodes can be securely added to a stratified topology to scale throughput without sacrificing anonymity.

cs.CR↗

arXiv2014

Censorship Resistance: Let a Thousand Flowers Bloom?

This paper argues that one of the most important decisions in designing and deploying censorship resistance systems is whether one set of system options should be selected (the best), or whether there should be several sets of good ones. We model the problem of choosing these options as a cat-and-mouse game and show that the best strategy depends on the value the censor associates with total system censorship versus partial, and the tolerance of false positives. If the censor has a low tolerance to false positives then choosing one censorship resistance system is best. Otherwise choosing several systems is the better choice, but the way traffic should be distributed over the systems depends on the tolerance of the censor to false negatives. We demonstrate that establishing the censor's utility function is critical to discovering the best strategy for censorship resistance.

cs.CR↗