  Phrase search allows retrieval of documents containing an exact phrase, which
plays an important role in many machine learning applications for cloud-based
IoT, such as intelligent medical data analytics. In order to protect sensitive
information from being leaked by service providers, documents (e.g., clinic
records) are usually encrypted by data owners before being outsourced to the
cloud. This, however, makes the search operation an extremely challenging task.
Existing searchable encryption schemes for multi-keyword search operations fail
to perform phrase search, as they are unable to determine the location
relationship of multiple keywords in a queried phrase over encrypted data on
the cloud server side. In this paper, we propose P3, an efficient
privacy-preserving phrase search scheme for intelligent encrypted data
processing in cloud-based IoT. Our scheme exploits the homomorphic encryption
and bilinear map to determine the location relationship of multiple queried
keywords over encrypted data. It also utilizes a probabilistic trapdoor
generation algorithm to protect users search patterns. Thorough security
analysis demonstrates the security guarantees achieved by P3. We implement a
prototype and conduct extensive experiments on real-world datasets. The
evaluation results show that compared with existing multikeyword search
schemes, P3 can greatly improve the search accuracy with moderate overheads.
