An Efficient Privacy-Preserving Ranked Multi-Keyword Retrieval for Multiple Data Owners in Outsourced Cloud
Dong Li, Jiahui Wu, Junqing Le, Qingguo Lü, Xiaofeng Liao, Tao Xiang
Abstract
With the widespread use of cloud storage technology by individuals and organizations, data providers usually send their data to cloud for storage to reduce memory pressure, and allow the users to retrieve these data, which has become the trend of rapid data retrieval. To guarantee the data confidentiality, several research works have been developed on encrypted cloud data for ranked multi-keyword retrieval. Nevertheless, most of these schemes are disabled since they cannot resist keyword guessing attacks. Moreover, the ranked top- <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> search results obtained by the subscriber from the encrypted cloud data are inaccurate. To overcome these drawbacks, we design a novel and efficient privacy-preserving ranked multi-keyword retrieval scheme (named as PRMKR) in this paper. With PRMKR, the data and the inverted indexes which belong to the data provider can be securely transferred to the cloud server. In addition, a registered subscriber can request accurate retrieval services without compromising his/her trapdoor information to the cloud server. Specifically, we design an encryption searchable plugin-in server and lower dimensional inverted indexesvector for data owners, which can further guarantee data confidentiality of the data owner and improve search efficiency, respectively. Our rigorous security proof demonstrates that PRMKR can withstand keyword guessing attacks. Finally, experimental evaluations confirm that PRMKR has decent computational and communication efficiency.