Katharina Kohls (Ruhr-University Bochum), Kai Jansen (Ruhr-University Bochum), David Rupprecht (Ruhr-University Bochum), Thorsten Holz (Ruhr-University Bochum), Christina Pöpper (New York University Abu Dhabi)

Traffic-analysis attacks are a persisting threat for Tor users. When censors or law enforcement agencies try to identify users, they conduct traffic-confirmation attacks and monitor encrypted transmissions to extract metadata—in combination with routing attacks, these attacks become sufficiently powerful to de-anonymize users. While traffic-analysis attacks are hard to detect and expensive to counter in practice, geographical avoidance provides an option to reject circuits that might be routed through an untrusted area. Unfortunately, recently proposed solutions introduce severe security issues by imprudent design decisions.

In this paper, we approach geographical avoidance starting from a thorough assessment of its challenges. These challenges serve as the foundation for the design of an empirical avoidance concept that considers actual transmission characteristics for justified decisions. Furthermore, we address the problems of untrusted or intransparent ground truth information that hinder a reliable assessment of circuits. Taking these features into account, we conduct an empirical simulation study and compare the performance of our novel avoidance concept with existing
approaches. Our results show that we outperform existing systems by 22 % fewer rejected circuits, which reduces the collateral damage of overly restrictive avoidance decisions. In a second evaluation step, we extend our initial system concept and implement the prototype MultilateraTor. This prototype is the first to satisfy the requirements of a practical deployment, as it maintains Tor’s original level of security, provides reasonable performance, and overcomes the fundamental security flaws of existing systems.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 34 ) ) ) [post__not_in] => Array ( [0] => 4562 ) )

Ginseng: Keeping Secrets in Registers When You Distrust the...

Min Hong Yun (Rice University), Lin Zhong (Rice University)

Read More

Understanding Open Ports in Android Applications: Discovery, Diagnosis, and...

Daoyuan Wu (Singapore Management University), Debin Gao (Singapore Management University), Rocky K. C. Chang (The Hong Kong Polytechnic University), En He (China Electronic Technology Cyber Security Co., Ltd.), Eric K. T. Cheng (The Hong Kong Polytechnic University), Robert H. Deng (Singapore Management University)

Read More

ExSpectre: Hiding Malware in Speculative Execution

Jack Wampler (University of Colorado Boulder), Ian Martiny (University of Colorado Boulder), Eric Wustrow (University of Colorado Boulder)

Read More

Time Does Not Heal All Wounds: A Longitudinal Analysis...

Meng Luo (Stony Brook University), Pierre Laperdrix (Stony Brook University), Nima Honarmand (Stony Brook University), Nick Nikiforakis (Stony Brook University)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)