Keyword private information retrieval (Keyword PIR) enables a client to retrieve the value associated with a keyword from a database while keeping the queried keyword private, thereby generalizing traditional private information retrieval, known as index PIR. The state-of-the-art by Hao et al. (USENIX 2025) has several limitations. First, their generic construction requires three invocations of an underlying index-PIR protocol. Second, to reduce this overhead, they propose a specialized design combining SimplePIR with hash tables. However, this approach inherits SimplePIR's large client-side hint, resulting in substantial per-database storage costs on the client side. Moreover, it allows clients to retrieve information beyond the value associated with the queried keyword.
In this work, we present a novel and practical Keyword PIR framework that addresses these limitations. Our construction extends the hintless KsPIR scheme of Luo et al. (CCS 2024) to the keyword setting, ensuring that a semi-honest client retrieves only the value corresponding to the queried keyword. The construction leverages the linear homomorphic technique of Peikert and Pepin (TCC 2025). To accelerate homomorphic evaluation, we design a baby-step giant-step (BSGS) implementation and an offline/online decomposition based on a Galois-theoretic formulation. Experimental results show a mean online speedup of over the generic framework when instantiated with the same index-PIR scheme, for databases containing up to entries.