As the aging population continues to grow, healthcare systems face increasing pressure to support timely and personalized medical services to older adults. To address this need, Aging in Place (AiP) has emerged as a promising approach, enabling seniors to remain in their own homes while still receiving continuous medical support. AiP services increasingly rely on similarity search over sensitive electronic health records (EHRs) to enable personalized interventions. To support such queries with limited local resources, medical institutions outsource EHRs to cloud servers. However, outsourcing raises three key challenges: preserving data and query privacy, hiding access patterns, and allowing flexible similarity measures. To address these challenges, we propose an efficient, privacy-preserving scheme for fine-grained similarity queries tailored to AiP. Our design combines p-stable locality-sensitive hashing (p-stable LSH) with additive secret sharing (ASS) and function secret sharing (FSS) to return all records that satisfy users’ fine-grained similarity requirements while ensuring privacy throughout the process. Our security analysis confirms privacy guarantees under the honest-but-curious model. Extensive experimental evaluations demonstrate computational efficiency and practical scalability for real-world AiP implementations.