Jodes: Efficient Oblivious Join in the Distributed Setting

2025-01-16

AI Summary

To address security and efficiency bottlenecks in distributed equality joins within trusted execution environments (TEEs), this paper proposes the first bidirectionally oblivious equality join protocol—simultaneously hiding both enclave memory access patterns and network traffic volume. Methodologically, it integrates garbled circuits, secret sharing, and hierarchical batching to construct distributed oblivious shuffle and join primitives. The protocol achieves asymptotically optimal communication and computation complexity while revealing only input and output sizes. Experimental evaluation on a 16-node cluster demonstrates up to a 6× throughput improvement over the state-of-the-art, while strictly preserving data privacy under strong security assumptions. Moreover, it supports complex analytical operations and, for the first time, realizes general-purpose bidirectional obliviousness for equality joins in TEEs.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Privacy

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Secure Data Analysis
• Fast Equality Joins
• Confidential Computing

Innovation

• Jodes Algorithm
• Secure Join Operation
• Performance Enhancement