BKPIR: Keyword PIR for Private Boolean Retrieval
Jie Song, Zhen Xu, Yan Zhang, Pengwei Zhan, Mingxuan Li, Shuai Ma, Ru Xie
Abstract
—Keyword Private Information Retrieval (Keyword PIR) enables users to retrieve data associated with specific keywords from a database while keeping their queries private. However, existing Keyword PIR schemes struggle to support the boolean retrieval model, which is essential for practical applications that require logical combinations of terms. This paper proposes a novel keyword PIR scheme leveraging advancements in homomorphic equality operations. It enables privacy-preserving retrieval over databases with many-to-many keyword-value mappings while supporting boolean operators for expressive search logic. Importantly, this extension preserves the core security guarantees of classical PIR. To the best of our knowledge, this is the first work to integrate keyword PIR with the boolean retrieval model. Experimental evaluation shows that our scheme achieves a communication cost reduction proportional to the total number of values in the many-to-many keyword-value database, along with aggregate query processing performance gains that scale linearly with the number of values. These improvements enhance its feasibility for real-world applications such as privacy-preserving web search and patent retrieval.
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