Topology Self-optimization for Anti-tracking Network via Nodes Distributed Computing

Anti-tracking network aims to protect the privacy of network users’ identities and communication relationship. The research of P2P-based anti-tracking network has attracted more and more attentions because of its decentralization, scalability, and widespread distribution. But, P2P-based anti-tracking network still faces the attacks on network structure which can destroy the usability of anti-tracking network effectively. So, a secure and resilient network structure is an important prerequisite to maintain the stability and security of anti-tracking network. In this paper, we propose a topology self-optimization method for anti-tracking network via nodes distributed computing. Based on convex-polytope topology (CPT), our proposal achieves topology self-optimization by each node optimizing its local topology in optimum structure. Through the collaboration of all nodes in network, the whole network topology will evolve into the optimum structure. Our experimental results show that the topology self-optimization method improves the network robustness and resilience of anti-tracking network when confronting to the dynamic network environment.