PECS: Privacy enhanced conjunctive search over encrypted data in the cloud supporting parallel search

The cost-effective storage and computing services offered by the cloud model have accelerated the rate of data outsourcing in domains like health industry and scientific research. Although cloud services provide multiple benefits, outsourcing sensitive information demands the assurance of security and privacy given that data and its access are not in control of the data owner. Searchable encryption is an active research area in the cloud model to improve the usage of outsourced encrypted data. In this paper, we propose a novel privacy enhanced conjunctive search (PECS) over encrypted data in the cloud supporting parallel search and dynamic updating to achieve search efficiency. To achieve this, first, we present a new tree-based partitioned index structure (TPIS) enabling parallel search to utilize the cloud resources efficiently. Parallel search is needed to retrieve data from the extensive collection of files outsourced. Second, we present two search schemes to provide query privacy and keyword privacy in the threat model. The proposed search method PECS satisfies the security and privacy goals of multi-keyword search over encrypted data. The results of our experiments show better search efficiency, indicating that the proposed conjunctive search scheme is suitable for practical use.