Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries over Encrypted Cloud Data
Abstract
Individuals can store their data on remote servers using cloud computing and allow access to data through cloud servers to public users. Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries over Encrypted Cloud Data As outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploading to the cloud. However, due to the difficulty of searching over encrypted data, this significantly limits the usability of outsourced data. We address this issue in this paper by developing multi-keyword fine-grained search schemes over encrypted cloud data.
Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries over Encrypted Cloud Data Our original contributions are threefold. First, we introduce the relevant scores and preference factors on keywords that enable precise keyword search and personalized user experience. Second, we are developing a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search of keywords mixed “AND,” “OR” and “NO.”
Advantages
- Better search results with multi-keyword query by the cloud server according to some ranking criteria.
- To reduce the communication cost.
- Achieves lower query complexity.
- Achieves better efficiency in index building scheme of our proposed model.
System Configuration
H/W System Configuration
Speed : 1.1 GHz
RAM : 256 MB(min)
Hard Disk : 20 GB
Floppy Drive : 1.44 MB
Key Board : Standard Windows Keyboard
Mouse : Two or Three Button Mouse
Monitor : SVGA
S/W System Configuration
Platform : cloud computing
Operating system : Windows Xp,7,
Server : WAMP/Apache
Working on : Browser Like Firefox, IE
Conclusion
We have investigated the issue of fine-grained multi-keyword search (FMS) over encrypted cloud data in this paper and proposed two FMS schemes. The FMS I includes both the relevance scores and keyword preference factors, respectively, to enhance more accurate search and better user experience.
