Proceedings ArticleDOI
Practical private information retrieval supporting keyword search in the cloud
Mengke Yu,Kaichen Yang,Lingbo Wei,Jinyuan Sun +3 more
- pp 1-6
TLDR
This paper introduces the mechanism of pricing to solve the problem of impractical cost and achieves the minimum communication and computation cost according to the flexible privacy and budget specified by users using the scheme called KSPIR.Abstract:
In cloud computing environment, just protecting the contents of the queries from users to a large database server is far away from enough. Because it does not protect the leak of access patterns from careful observations. It is thus important to make sure the server learning nothing about the queries including access patterns. However, this implies an expensive computation or communication cost of all the data on the server. Existing solutions are not efficient due to their impractical communication and computation cost. Besides, most of them do not support keyword search. In this paper, we introduce the mechanism of pricing to solve the problem of impractical cost. Using our scheme called KSPIR, we achieve the minimum communication and computation cost according to the flexible privacy and budget specified by users. It is indeed a kind of tradeoff between the cost of retrieval and the degree of privacy. It is worth noting that it also supports keyword search. It allows users to retrieve the data items containing the keywords they are interested in. The experimental results confirm the correctness and efficiency of KSPIR.read more
Citations
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Journal ArticleDOI
Privacy-Preserving Fine-Grained Data Retrieval Schemes for Mobile Social Networks
Mohamed E. Mahmoud,Khaled Rabieh,Ahmed Sherif,Enahoro Oriero,Muhammad Ismail,Erchin Serpedin,Khalid A. Qaraqe +6 more
TL;DR: Analysis and implementation results demonstrate that the proposed privacy-preserving fine-grained data retrieval schemes for mobile social networks (MSNs) can preserve the privacy of the MSN users with high performance.
Posted Content
Achieving Fine-grained Multi-keyword Ranked Search over Encrypted Cloud Data.
Guowen Xu,Hongwei Li +1 more
TL;DR: Through security analysis, it can prove that the data confidentiality, privacy protection of index and trapdoor, and the unlinkability of trapdoor can be achieved in the Fine-grained Multi-keyword Ranked Search (FMRS) scheme.
References
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Book ChapterDOI
Public-key cryptosystems based on composite degree residuosity classes
TL;DR: A new trapdoor mechanism is proposed and three encryption schemes are derived : a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA, which are provably secure under appropriate assumptions in the standard model.
Journal ArticleDOI
Private information retrieval
TL;DR: This work describes schemes that enable a user to access k replicated copies of a database and privately retrieve information stored in the database, so that each individual server gets no information on the identity of the item retrieved by the user.
Proceedings ArticleDOI
Searchable symmetric encryption: improved definitions and efficient constructions
TL;DR: In this paper, the authors proposed a searchable symmetric encryption (SSE) scheme for the multi-user setting, where queries to the server can be chosen adaptively during the execution of the search.
Proceedings ArticleDOI
Private information retrieval
TL;DR: Schemes that enable a user to access k replicated copies of a database and privately retrieve information stored in the database and get no information on the identity of the item retrieved by the user are described.
Journal ArticleDOI
Searchable symmetric encryption: Improved definitions and efficient constructions
TL;DR: This paper begins by reviewing existing notions of security and proposes new and stronger security definitions, and presents two constructions that show secure under these new definitions and are more efficient than all previous constructions.