Jodes：分布式环境下的高效不经意连接算法
Jodes: Efficient Oblivious Join in the Distributed Setting
AI总结：
针对分布式TEE分析系统的访问模式与传输量泄露问题，提出兼具高安全性与低渐近开销的高效不经意连接算法Jodes，在16服务器实验中性能较最优方案最高提升6倍。
摘要：
可信执行环境（TEE）为构建云端分析系统提供了隔离的安全环境，但仍存在由侧信道攻击导致的访问模式泄露问题。为更好地保护数据安全，TEE 飞地内的计算需要实现不经意性，这会带来显著开销并严重拖慢计算速度。一种天然的提速方式是在分布式环境中用多台服务器搭建分析系统，但该环境带来了新的安全隐患——服务器之间的传输数据量可能向网络攻击者泄露敏感信息。现有研究已设计专门算法解决这一问题，但它们对等连接（最重要却颇具难度的数据库算子之一）的支持要么效率低下、功能受限，要么基于较弱的安全假设。\n本文提出 Jodes，一种适用于分布式环境的高效不经意连接算法。Jodes 可同时防止网络侧和飞地侧的信息泄露，支持通用的等连接操作，提供仅公开输入规模和输出规模的高安全级别保护。同时，其通信开销和计算开销在渐近意义上均优于现有算法。为验证 Jodes 的实用性，我们在包含 16 台服务器的分布式环境中开展实验。实验结果表明，相比当前最优的连接算法，Jodes 的性能提升最高可达 6 倍。
英文摘要：
Trusted execution environment (TEE) has provided an isolated and secure environment for building cloud-based analytic systems, but it still suffers from access pattern leakages caused by side-channel attacks. To better secure the data, computation inside TEE enclave should be made oblivious, which introduces significant overhead and severely slows down the computation. A natural way to speed up is to build the analytic system with multiple servers in the distributed setting. However, this setting raises a new security concern -- the volumes of the transmissions among these servers can leak sensitive information to a network adversary. Existing works have designed specialized algorithms to address this concern, but their supports for equi-join, one of the most important but non-trivial database operators, are either inefficient, limited, or under a weak security assumption. In this paper, we present Jodes, an efficient oblivious join algorithm in the distributed setting. Jodes prevents the leakage on both the network and enclave sides, supports a general equi-join operation, and provides a high security level protection that only publicizes the input sizes and the output size. Meanwhile, it achieves both communication cost and computation cost asymptotically superior to existing algorithms. To demonstrate the practicality of Jodes, we conduct experiments in the distributed setting comprising 16 servers. Empirical results show that Jodes achieves up to a sixfold performance improvement over state-of-the-art join algorithms.
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