Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy.
. Specifically, we explore the statistical measure approach, i.e., relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys “as-strong-as-possible” security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.
Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords without first decrypting it, these techniques support only conventional Boolean keyword search,1 without capturing any relevance of the files in the search result. When directly applied in large collaborative data outsourcing cloud environment, they may suffer from the following two main drawbacks. On the one hand, for each search request, users without pre-knowledge of the encrypted cloud data have to go through every retrieved file in order to find ones most matching their interest, which demands possibly large amount of post processing overhead; On the other hand, invariably sending back all files solely based on presence/absence of the keyword further incurs large unnecessary network traffic, which is absolutely undesirable in today’s pay-as-you-use cloud paradigm.
Our work is among the first few ones to explore ranked search over encrypted data in Cloud Computing. Ranked search greatly enhances system usability by returning the matching files in a ranked order regarding to certain relevance criteria (e.g., keyword frequency), thus making one step closer toward practical deployment of privacy-preserving data hosting services in the context of Cloud Computing. To achieve our design goals on both system security and usability, we propose to bring together the advance of both crypto and IR community to design the ranked searchable symmetric encryption (RSSE) scheme, in the spirit of “as-strong-as-possible” security guarantee.
Specifically, we explore the statistical measure approach from IR and text mining to embed weight information (i.e., relevance score) of each file during the establishment of searchable index before outsourcing the encrypted file collection. As directly outsourcing relevance scores will leak lots of sensitive frequency information against the keyword privacy, we then integrate a recent crypto primitive  order-preserving symmetric encryption (OPSE) and properly modify it to develop a one-to-many order-preserving mapping technique for our purpose to protect those sensitive weight information, while providing efficient ranked search functionalities.
- Admin Login
- User Login
- Uploading Of Files
- Keyword Searching
- Encryption of Words
- Downloading Files
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