Enhancing Noise Estimation for Statistical Disclosure Attacks Using the Artificial Bee Colony Algorithm
Aksoy A, Kesdogan D (2025)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2025
Journal
Publisher: Springer
Series: Lecture Notes in Computer Science
City/Town: Cham
Book Volume: 15396
Pages Range: 447-466
Conference Proceedings Title: Secure IT Systems. 29th Nordic Conference, NordSec 2024 Karlstad, Sweden, November 6–7, 2024 Proceedings
Event location: Karlstad
ISBN: 9783031790065
DOI: 10.1007/978-3-031-79007-2_23
Abstract
The Statistical Disclosure Attack (SDA) is an effective technique for de-anonymizing users in anonymous communication networks. It was presented as a signal detection problem, aiming to distinguish the signal (messaging partners of the targeted user) from the noisy channel (all users). Although the success of signal detection relies on a better understanding of noise properties, the SDA lacks a specific method to analyze the background noise present in the targeted user’s signal, i.e., a noise estimation method. Several noise estimation methods have been proposed in previous studies by utilizing communication rounds in which the targeted user does not participate, called noise rounds. However, these approaches treat these rounds equally without considering their relation with background noise, limiting their effectiveness. In this paper, we propose a novel noise estimation method that weights the noise rounds based on their relation with the background noise present in the targeted user’s signal. We formulate this approach as an optimization problem, where the objective function measures the difference between the current noise estimation and the actual background noise. We minimize this difference by optimizing weights using the Artificial Bee Colony (ABC) algorithm. Our findings indicate that converging noise estimation to the background noise improves the accuracy of the attack.
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Germany (DE)How to cite
APA:
Aksoy, A., & Kesdogan, D. (2025). Enhancing Noise Estimation for Statistical Disclosure Attacks Using the Artificial Bee Colony Algorithm. In Leonardo Horn Iwaya, Liina Kamm, Leonardo Martucci, Tobias Pulls (Eds.), Secure IT Systems. 29th Nordic Conference, NordSec 2024 Karlstad, Sweden, November 6–7, 2024 Proceedings (pp. 447-466). Karlstad, SE: Cham: Springer.
MLA:
Aksoy, Alperen, and Dogan Kesdogan. "Enhancing Noise Estimation for Statistical Disclosure Attacks Using the Artificial Bee Colony Algorithm." Proceedings of the 29th Nordic Conference on Secure IT Systems, NordSec 2024, Karlstad Ed. Leonardo Horn Iwaya, Liina Kamm, Leonardo Martucci, Tobias Pulls, Cham: Springer, 2025. 447-466.
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