Privacy-preserving ranked search over encrypted cloud data
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Örencik, Cengiz (2014) Privacy-preserving ranked search over encrypted cloud data. [Thesis]
Official URL: http://risc01.sabanciuniv.edu/record=b1589828 (Table of Contents)
Search over encrypted data recently became a critical operation that raised a considerable amount of interest in both academia and industry, especially as outsourcing sensitive data to cloud proves to be a strong trend to benefit from the unmatched storage and computing capacities thereof. Indeed, privacy-preserving search over encrypted data, an apt term to address privacy related issues concomitant in outsourcing sensitive data, has been widely investigated in the literature under different models and assumptions. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. Some of those privacy issues can be summarized as: submitted search terms and their frequencies, returned responses and their relevancy to the query, and retrieved data items may all contain sensitive information about the users. In this thesis, we propose two di erent multi-keyword search schemes that ensure users' privacy against both external adversaries including other authorized users and cloud server itself. The proposed schemes use cryptographic techniques as well as query and response randomization. Provided that the security and randomization parameters are appropriately chosen, both the search terms in the queries and the returned responses are protected against privacy violations. The scheme implements strict security and privacy requirements that essentially can hide similarities between the queries that include the same keywords. One of the main advantages of all the proposed methods in this work is the capability of multi-keyword search in a single query. We also incorporate effective ranking capabilities in the proposed schemes that enable user to retrieve only the top matching results. Our comprehensive analytical study and extensive experiments using both real and synthetic data sets demonstrate that the proposed schemes are privacy-preserving, effective, and highly efficient.
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