Research On Query Techniques For Encrypted Outsourced Data

In this era of big data,data generated by all sorts of means grows explosively.It has become an irresistible trend that companies,organizations,and even individual users outsource their data to public cloud providers in exchange for relief from the data management burden.Despite the advantage brought by data outsourcing,it brings new security and privacy concerns as the data owners lose direct control of sensitive data.The outsourced data face attacks from both insiders(e.g.,Cloud Service Provider,CSP)and outsiders.Traditional data encryption methods can ensure outsourced data security but inhibit direct data utilization.Thus,query techniques based on searchable encryption(SE)gain significant research value,as they enable effective searches over encrypted data.SE-based query techniques have been extensively studied for years.However,new challenges occur with the widespread usage of online search services,such as urgent needs for higher query accuracy and support for combined search terms.The existing schemes are unable to meet the demand as they are constrained by the index design and encryption method.In the meanwhile,the swift development of unconventional cloud-computing environments,such as edge computing and Vehicular Ad hoc Networks(VANETs),adds new functional requirements to query schemes.In those environments,merely providing a query function is not enough to fulflll an overall secure search.Therefore,additional support for functionalities like access control and data update can be indispensable.In conclusion,query schemes are expected to provide enough privacy protection while achieving better flexibility,higher performance,lower overhead,and more functionality.Motivated by the above limitations and problems,this dissertation focuses on enriching the query expressiveness and the functionality for secure search over outsourced data.The innovations and contributions of this dissertation can be summarized as follows.(1)Apart from query requirements,access control over outsourced data can also be a necessity in certain scenarios.Unfortunately,most previous schemes fail to provide lightweight access control along with an effective multi-keyword search over encrypted outsourced data.In light of this,we propose an efficient and privacy-preserving Multikeyword Ranked Search scheme with Fine-grained access control(MRSF).MRSF can realize highly accurate ciphertext retrieval by combining coordinate matching with Term Frequency-Inverse Document Frequency(TF-IDF)and improving the secure k-Nearest Neighbor(kNN)method.Besides,it can effectively refine users’ search privileges by utilizing the polynomial-based access strategy.Formal security analysis shows that MRSF is secure in terms of confidentiality of outsourced data and the privacy of index and tokens.Extensive experiments further show that,compared with existing schemes,MRSF achieves higher search accuracy and more functionalities efficiently.(2)Most existing ranked keyword search schemes quantify the similarity between the documents and queries by conducting keyword matching,which ignores the semantic relations and incurs high computational overhead.To solve this challenging issue,we design a Semantic-aware Ranked Multi-keyword Search scheme with Verification(SRMSV).The proposed scheme realizes secure and semantic-aware search by adopting the Latent Dirichlet Allocation(LDA)topic model and the Chinese remainder theorem-based secret sharing mechanism.Considering that the cloud server may be malicious,SRMSV implements a verification mechanism to verify the correctness and completeness of search results.Formal security analysis proves that the search scheme and verification mechanism of SRMSV are secure in both the known ciphertext model and the known background model.Real-world dataset experiment results demonstrate that SRMSV is efficient and feasible in practical applications.(3)Spatial Keyword Search(SKS)plays a fundamental role in LBS(Location-Based Service)-associated applications,especially in VANETs.There is a need to achieve both privacy guarantee and search flexibility for SKS,but the existing SKS solutions are inadequate..To fill up shortages of the previous researches,we propose a Secure Semantic-aware Spatial Keyword Search scheme that supports Dynamic update(3 SKSD).Specifically,the LDA topic model and secure kNN method are leveraged to build an encrypted R-tree structure for spatial objects,facilitating secure SKS and dynamic updates.Moreover,we propose an advanced scheme with forward security on the basis of 3SKSD,aiming at minimizing the privacy leakage caused by dynamic updates.Our formal security analysis verifies the validity and security of 3SKSD.Meanwhile,the experimental evaluation demonstrates its high search accuracy and efficiency.