ADAT: Adaptive Dynamic Anonymity and Traceability via Privacy-Aware Random Forest and Truncated Local Differential Privacy in a Trusted Execution Environment (TEE)

In current mobile networks, users’ identity privacy is threatened by long-term observation attacks. To resist such attacks, identity-anonymity technology has been proposed. However, existing anonymity schemes cannot adapt to diverse, dynamic business scenarios because of their rigid anonymity strategies. This leads to wasted computing and communication resources in low-risk scenarios or privacy leaks in high-risk scenarios. To address this problem, we propose an Adaptive Dynamic Anonymity and Traceability scheme based on privacy-aware random forest and local differential privacy in a Trusted Execution Environment. We first construct a convex optimization model to seek the optimal balance between privacy risk and performance cost. Subsequently, we train a privacy-aware random forest model to intelligently predict the optimal Time-To-Live of the anonymous identifier based on the real-time context. Lastly, to resist long-term observation attacks, our scheme uses a lightweight symmetric encryption algorithm to generate pseudo-random, anonymous identifiers and applies truncated local differential privacy to ensure the indistinguishability of the timing patterns of anonymous identifier updates. We formally prove that our scheme can resist long-term observation attacks. Experimental results show that, compared with fixed Time-To-Live schemes, our scheme significantly reduces the comprehensive cost while maintaining the same level of security. Furthermore, compared with traditional public-key schemes, it greatly improves the generation speed of anonymous identifiers and reduces communication costs.