ENABLING FINE-GRAINED MULTI-KEYWORD SEARCH SUPPORTING CLASSIFIED SUB-DICTIONARIES OVER ENCRYPTED CLOUD DATA
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This document proposes schemes for fine-grained multi-keyword search over encrypted cloud data. It introduces relevance scores and preference factors to enable precise keyword search and personalized experiences. An efficient scheme supports complex logic searches using mixed Boolean operators. Classified sub-dictionaries improve index building, trapdoor generation, and query efficiency. Security analysis and experiments demonstrate the proposed schemes achieve the same security as existing methods with better performance.
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
and better performance in terms of functionality, query complexity and
efficiency.
CONCLUSION
In this paper, we have investigated on the fine-grained multi-keyword search
(FMS) issue over encrypted cloud data, and proposed two FMS schemes. The
FMS_I includes both the relevance scores and the preference factors of
keywords to enhance more precise search and better users’ experience,
respectively. The FMS_II achieves secure and efficient search with practical
functionality, i.e., “AND”, “OR” and “NO” operations of keywords.
Furthermore, we have proposed the enhanced schemes supporting classified
sub-dictionaries (FMSCS) to improve efficiency. For the future work, we intend
to further extend the proposal to consider the extensibility of the file set and
the multi-user cloud environments. Towards this direction, we have made
some preliminary results on the extensibility and the multi-user cloud
environments [20]. Another interesting topic is to develop the highly scalable
searchable encryption to enable efficient search on large practical databases.
REFERENCES
[1] H. Liang, L. X. Cai, D. Huang, X. Shen, and D. Peng, “An smdpbased service
model for interdomain resource allocation in mobile cloud networks,” IEEE
Trans. Veh. Technol., vol. 61, no. 5, pp. 2222–2232, Jun. 2012.](https://image.slidesharecdn.com/enablingfine-grainedmulti-keywordsearchj-160722051105/85/ENABLING-FINE-GRAINED-MULTI-KEYWORD-SEARCH-SUPPORTING-CLASSIFIED-SUB-DICTIONARIES-OVER-ENCRYPTED-CLOUD-DATA-2-320.jpg)
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
[2] M. M. Mahmoud and X. Shen, “A cloud-based scheme for protecting
source-location privacy against hotspot-locating attack in wireless sensor
networks,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 10, pp. 1805–1818,
Oct. 2012.
[3] Q. Shen, X. Liang, X. Shen, X. Lin, and H. Luo, “Exploiting geodistributed
clouds for e-health monitoring system with minimum service delay and privacy
preservation,” IEEE J. Biomed. Health Inform., vol. 18, no. 2, pp. 430–439, Mar.
2014.
[4] H. Li, Y. Dai, L. Tian, and H. Yang, “Identity-based authentication for cloud
computing,” in Cloud Computing, Springer, 2009, pp. 157–166.
[5] T. Jung, X. Mao, X. Li, S.-J. Tang, W. Gong, and L. Zhang, “Privacy-preserving
data aggregation without secure channel: Multivariate polynomial evaluation,”
in Proc. IEEE INFOCOM, 2013, pp. 2634–2642.
[6] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy-preserving multi-
keyword ranked search over encrypted cloud data,” IEEE Trans. Parallel Distrib.
Syst., vol. 25, no. 1, pp. 222–233, Jan. 2014.
[7] https://support.google.com/websearch/answer/173733?hl=en, 2014.
[8] D. X. Song, D. Wagner, and A. Perrig, “Practical techniques for searches on
encrypted data,” in Proc. S&P, IEEE, 2000, pp. 44–55.](https://image.slidesharecdn.com/enablingfine-grainedmulti-keywordsearchj-160722051105/85/ENABLING-FINE-GRAINED-MULTI-KEYWORD-SEARCH-SUPPORTING-CLASSIFIED-SUB-DICTIONARIES-OVER-ENCRYPTED-CLOUD-DATA-3-320.jpg)
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
[9] R. Li, Z. Xu, W. Kang, K. C. Yow, and C.-Z. Xu, “Efficient multikeyword ranked
query over encrypted data in cloud computing,” Future Generation Comput.
Syst., vol. 30, pp. 179–190, 2014.
[10] H. Li, D. Liu, Y. Dai, T. H. Luan, and X. Shen, “Enabling efficient multi-
keyword ranked search over encrypted cloud data through blind storage,” IEEE
Trans. Emerging Topics Comput., 2014, DOI:10.1109/TETC.2014.2371239.
[11] C. Wang, N. Cao, J. Li, K. Ren, and W. Lou, “Secure ranked keyword search
over encrypted cloud data,”](https://image.slidesharecdn.com/enablingfine-grainedmulti-keywordsearchj-160722051105/85/ENABLING-FINE-GRAINED-MULTI-KEYWORD-SEARCH-SUPPORTING-CLASSIFIED-SUB-DICTIONARIES-OVER-ENCRYPTED-CLOUD-DATA-4-320.jpg)
ENABLING FINE-GRAINED MULTI-KEYWORD SEARCH SUPPORTING CLASSIFIED SUB-DICTIONARIES OVER ENCRYPTED CLOUD DATA
- 1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com ENABLING FINE-GRAINED MULTI-KEYWORD SEARCH SUPPORTING CLASSIFIED SUB-DICTIONARIES OVER ENCRYPTED CLOUD DATA ABSTRACT Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine- grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones
- 2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com and better performance in terms of functionality, query complexity and efficiency. CONCLUSION In this paper, we have investigated on the fine-grained multi-keyword search (FMS) issue over encrypted cloud data, and proposed two FMS schemes. The FMS_I includes both the relevance scores and the preference factors of keywords to enhance more precise search and better users’ experience, respectively. The FMS_II achieves secure and efficient search with practical functionality, i.e., “AND”, “OR” and “NO” operations of keywords. Furthermore, we have proposed the enhanced schemes supporting classified sub-dictionaries (FMSCS) to improve efficiency. For the future work, we intend to further extend the proposal to consider the extensibility of the file set and the multi-user cloud environments. Towards this direction, we have made some preliminary results on the extensibility and the multi-user cloud environments [20]. Another interesting topic is to develop the highly scalable searchable encryption to enable efficient search on large practical databases. REFERENCES [1] H. Liang, L. X. Cai, D. Huang, X. Shen, and D. Peng, “An smdpbased service model for interdomain resource allocation in mobile cloud networks,” IEEE Trans. Veh. Technol., vol. 61, no. 5, pp. 2222–2232, Jun. 2012.
- 3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com [2] M. M. Mahmoud and X. Shen, “A cloud-based scheme for protecting source-location privacy against hotspot-locating attack in wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 10, pp. 1805–1818, Oct. 2012. [3] Q. Shen, X. Liang, X. Shen, X. Lin, and H. Luo, “Exploiting geodistributed clouds for e-health monitoring system with minimum service delay and privacy preservation,” IEEE J. Biomed. Health Inform., vol. 18, no. 2, pp. 430–439, Mar. 2014. [4] H. Li, Y. Dai, L. Tian, and H. Yang, “Identity-based authentication for cloud computing,” in Cloud Computing, Springer, 2009, pp. 157–166. [5] T. Jung, X. Mao, X. Li, S.-J. Tang, W. Gong, and L. Zhang, “Privacy-preserving data aggregation without secure channel: Multivariate polynomial evaluation,” in Proc. IEEE INFOCOM, 2013, pp. 2634–2642. [6] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy-preserving multi- keyword ranked search over encrypted cloud data,” IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 1, pp. 222–233, Jan. 2014. [7] https://support.google.com/websearch/answer/173733?hl=en, 2014. [8] D. X. Song, D. Wagner, and A. Perrig, “Practical techniques for searches on encrypted data,” in Proc. S&P, IEEE, 2000, pp. 44–55.
- 4. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com [9] R. Li, Z. Xu, W. Kang, K. C. Yow, and C.-Z. Xu, “Efficient multikeyword ranked query over encrypted data in cloud computing,” Future Generation Comput. Syst., vol. 30, pp. 179–190, 2014. [10] H. Li, D. Liu, Y. Dai, T. H. Luan, and X. Shen, “Enabling efficient multi- keyword ranked search over encrypted cloud data through blind storage,” IEEE Trans. Emerging Topics Comput., 2014, DOI:10.1109/TETC.2014.2371239. [11] C. Wang, N. Cao, J. Li, K. Ren, and W. Lou, “Secure ranked keyword search over encrypted cloud data,”