Title | Privacy-Preserving Fuzzy Multi-Keyword Search for Multiple Data Owners in Cloud Computing |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Chen, Lvhao, Liao, Xiaofeng, Mu, Nankun, Wu, Jiahui, Junqing, Junqing |
Conference Name | 2019 IEEE Symposium Series on Computational Intelligence (SSCI) |
Keywords | BF, Bloom filter, ciphertext search, cloud computing, cloud server, Computing Theory and Privacy, cryptography, data privacy, encrypted documents, Encryption, encryption method, fuzzy multi-keyword search., fuzzy set theory, Human Behavior, Indexes, information leakage, locality-sensitive hashing, LSH, multiple data owners, plaintext search techniques, Privacy-preserving, privacy-preserving fuzzy multikeyword search, pubcrawl, query processing, Resiliency, Scalability, search privacy, searchable encryption schemes, Servers, single data owner model |
Abstract | With cloud computing's development, more users are decide to store information on the cloud server. Owing to the cloud server's insecurity, many documents should be encrypted to avoid information leakage before being sent to the cloud. Nevertheless, it leads to the problem that plaintext search techniques can not be directly applied to the ciphertext search. In this case, many searchable encryption schemes based on single data owner model have been proposed. But, the actual situation is that users want to do research with encrypted documents originating from various data owners. This paper puts forward a privacy-preserving scheme that is based on fuzzy multi-keyword search (PPFMKS) for multiple data owners. For the sake of espousing fuzzy multi-keyword and accurate search, secure indexes on the basis of Locality-Sensitive Hashing (LSH) and Bloom Filter (BF)are established. To guarantee the search privacy under multiple data owners model, a new encryption method allowing that different data owners have diverse keys to encrypt files is proposed. This method also solves the high cost caused by inconvenience of key management. |
DOI | 10.1109/SSCI44817.2019.9003109 |
Citation Key | chen_privacy-preserving_2019 |