Proceedings ArticleDOI
Fuzzy Keyword Search over Encrypted Data in Cloud Computing
Jin Li,Qian Wang,Cong Wang,Ning Cao,Kui Ren,Wenjing Lou +5 more
- pp 441-445
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TLDR
This paper formalizes and solves the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy, and exploits edit distance to quantify keywords similarity and develops an advanced technique on constructing fuzzy keyword sets, which greatly reduces the storage and representation overheads.Abstract:
As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users' searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing fuzzy keyword sets, which greatly reduces the storage and representation overheads. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search.read more
Citations
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Journal ArticleDOI
Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data
TL;DR: This paper proposes a basic idea for the MRSE based on secure inner product computation, and gives two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models and further extends these two schemes to support more search semantics.
Journal ArticleDOI
A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data
TL;DR: This paper constructs a special tree-based index structure and proposes a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents.
Proceedings ArticleDOI
Secure Ranked Keyword Search over Encrypted Cloud Data
TL;DR: This paper defines and solves the problem of effective yet secure ranked keyword search over encrypted cloud data, and proposes a definition for ranked searchable symmetric encryption, and gives an efficient design by properly utilizing the existing cryptographic primitive, order-preserving asymmetric encryption (OPSE).
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Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement
TL;DR: This paper study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing with the help of semantic ontology WordNet, and proposes two PRSE schemes for different search intentions.
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