Empirical mixnet design

Mixing networks (mixnets) are cryptographic protocols designed to enhance the privacy and anonymity of online communications. Despite their robust privacy features, mixnets remain vulnerable to cyber attacks, such as adversaries compromising mixing nodes (mixes) to track message flows. To mitigate these threats, we propose an empirical mechanism design framework for identifying mixnet configurations that satisfy the designer’s goals. We first introduce two models for mixnets: one with heterogeneous mixes and another with layer-wise homogeneous mixes, capturing the diverse scales and complexities of real-world mixnets. The heterogeneous model allows each mix to have different properties, offering greater expressiveness but posing challenges for scalability of analysis. In contrast, the homogeneous model provides a simplified, yet practical, framework for large-scale analysis. Leveraging these models, we conceptualize adversarial attacks and defensive measures as strategic interactions between an attacker and a defender in a game. We demonstrate how our framework generates data that is useful for a mechanism designer trading off cost versus performance in deploying a mixnet.