Toward Secure Multikeyword Top-k Retrieval over Encrypted Cloud Data
Jul / Aug 2013
Cloud computing has emerging as a promising pattern for data outsourcing and high-quality data services. However, concerns of sensitive information on cloud potentially causes privacy problems. Data encryption protects data security to some extent, but at the cost of compromised efficiency. Searchable symmetric encryption (SSE) allows retrieval of encrypted data over cloud. In this paper, we focus on addressing data privacy issues using SSE. For the first time, we formulate the privacy issue from the aspect of similarity relevance and scheme robustness. We observe that server-side ranking based on order-preserving encryption (OPE) inevitably leaks data privacy. To eliminate the leakage, we propose a two-round searchable encryption (TRSE) scheme that supports top-k multikeyword retrieval. In TRSE, we employ a vector space model and homomorphic encryption. The vector space model helps to provide sufficient search accuracy, and the homomorphic encryption enables users to involve in the ranking while the majority of computing work is done on the server side by operations only on ciphertext. As a result, information leakage can be eliminated and data security is ensured. Thorough security and performance analysis show that the proposed scheme guarantees high security and practical efficiency.
ÃƒÂ¯Ã†â€™Ã‹Å“ Existing schemes focused on security definitions and encryption efficiency, and these works support only Boolean keyword retrieval without ranking.
ÃƒÂ¯Ã†â€™Ã‹Å“ Secure rank-ordered retrieval is proposed with improved searchable encryption in the scenario of the data center. They built a framework for privacy-preserving top-k retrieval, including secure indexing and ranking with OPE.
ÃƒÂ¯Ã†â€™Ã‹Å“ A ranking model is proposed to guarantee privacy-preserving document exchange among collaboration groups, which allows for privacy-preserving top-k retrieval from an outsourced inverted index.
ÃƒÂ¯Ã†â€™Ã‹Å“ Top-k retrieval over encrypted data is proposed on the basis of SSE, one-to-many OPM to further improve the efficiency.
ÃƒÂ¯Ã†â€™Ã‹Å“ The problem of top-k multikeyword retrieval over encrypted cloud data is handled by coordinate matching and inner product similarity to measure and evaluate the relevance scoring.
ÃƒÂ¯Ã†â€™Ã‹Å“ Homomorphism is employed to preserve the data privacy. They devised a secure protocol for processing k-nearest-neighbor (kNN) index query, thus preserving both the data privacy of the owner and the query privacy of the client.
ÃƒÂ¯Ã†â€™Ã‹Å“ Support only single keyword retrieval
ÃƒÂ¯Ã†â€™Ã‹Å“ Under top k retrieval security guarantee and retrieval accuracy are slightly weakened
ÃƒÂ¯Ã†â€™Ã‹Å“ Topk retrieval employed Boolean representation in their searchable index. Thus, files that share queried keywords have the same score, weakens the effectiveness of data utilization.
ÃƒÂ¯Ã†â€™Ã‹Å“ The concepts of similarity relevance and scheme robustness to formulate the privacy issue in searchable encryption schemes is proposed, and then solve the insecurity problem by proposing a two-round searchable encryption (TRSE) scheme.
ÃƒÂ¯Ã†â€™Ã‹Å“ Novel technologies in the cryptography community and information retrieval (IR) community are employed, including homomorphic encryption and vector space model.
ÃƒÂ¯Ã†â€™Ã‹Å“ The majority of computing work is done on the cloud while the user takes part in ranking, which guarantees top-k multikeyword retrieval over encrypted cloud data with high security and practical efficiency.
ÃƒÂ¯Ã†â€™Ã‹Å“ Data privacy
ÃƒÂ¯Ã†â€™Ã‹Å“ supports multikeyword top-k retrieval
ÃƒÂ¯Ã†â€™Ã‹Å“ guarantees high data privacy