In the shuffle model of DP (Differential Privacy), a shuffler randomly permutes users' data to achieve high accuracy and privacy. Recent studies show that most existing shuffle protocols are vulnerable to collusion attacks by the data collector and users. They address this issue by introducing the augmented shuffle model that incorporates random sampling and dummy data addition into the shuffler. However, it remains open how to ensure the shuffler follows the protocol and does not collude with the data collector in this model. We address this trust issue by thoroughly exploring the augmented shuffle model with TEEs (Trusted Execution Environments). We first introduce a new privacy notion, FODP (Fully Oblivious DP), which strengthens DP to prevent various TEE side-channel attacks based on external/internal memory access patterns and control flows. We propose a general framework for FODP algorithms based on memory-size obfuscation and three concrete algorithms within it. We also improve the efficiency of our algorithms by using the count-min sketch and optimizing the number of hashes. We evaluate our algorithms on Intel SGX and demonstrate their effectiveness through comparisons with nine baselines.

翻译：在差分隐私（DP）的混洗模型中，混洗器随机打乱用户数据以实现高精度与高隐私保护。最新研究表明，现有大多数混洗协议易受数据收集者与用户之间的合谋攻击。针对该问题，学界提出增强混洗模型，通过在混洗器中引入随机采样与虚拟数据添加来规避攻击。然而，在该模型下如何确保混洗器严格遵循协议且不信任数据收集者，仍是未解难题。我们通过全面探索结合可信执行环境（TEE）的增强混洗模型来解决这一信任问题。首先提出新型隐私概念 FODP（完全 oblivious 差分隐私），该概念通过强化差分隐私机制，抵御基于外部/内部内存访问模式及控制流的各类 TEE 侧信道攻击。我们构建了基于内存容量混淆的 FODP 算法通用框架，并在此框架内提出三种具体算法。通过采用计数最小草图并优化哈希函数数量，我们进一步提升了算法效率。最后在 Intel SGX 平台上对算法进行评测，通过与九种基线方法的对比验证其有效性。