Conflux: A High-Performance Keyword Private Retrieval System for Dynamic Datasets

Homomorphic Encryption (HE)-based Private Information Retrieval (PIR) allows clients to retrieve plaintext records from untrusted servers without revealing query content. While promising in theory, existing solutions fall short in practice due to two fundamental limitations: (1) poor support for dynamic datasets, which limits applicability in real-world, evolving workloads; and (2) excessive I/O overhead from full-database scans. These bottlenecks prevent current designs from bridging the gap between cryptographic privacy and system efficiency. In this paper, we introduce Conflux, an efficient keyword PIR system for dynamic data environments through a protocolarchitecture co-design approach. At the protocol level, Conflux employs a novel two-phase retrieval mechanism, consisting of an oblivious filtering phase followed by a precise retrieval phase. This design natively supports efficient online insertions, deletions, and updates, while maintaining near-optimal computational complexity. At the system level, Conflux adopts a heterogeneous accelerator architecture that tightly couples computational storage devices and incorporates software-hardware co-optimization techniques to mitigate I/O bottlenecks. Experimental results show that Conflux reduces query processing time by up to$2.64 \times$compared to the state-of-the-art methods, while retaining full support for dynamic datasets.