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A Novel Securable Fuzzy Logic Based Ranking Scheme for Document Searching on Outsourced Cloud Data

  • S. N. ManoharanEmail author
  • K. Ruba Soundar
Article
  • 12 Downloads

Abstract

Most of the existing searchable encryption scheme allows the user to search for the encrypted data with the support of Boolean search and this scheme are not so much effective to meet the data requirements of the user in the presence of large amount of documents in cloud. In this paper, we propose an effective Securable Fuzzy Logic based Ranking mechanism for document searching on outsourced cloud data. Our approach employs ECC based encryption and a fuzzy logic based ranking scheme over the encrypted-data to retrieve the documents from the cloud. The newly developed fuzzy logic based ranking scheme adopts six query-expansion (QE) ‘terms selection’ methods for computing the degrees of all the unique-terms comprised in the top retrieved document. Further, our fuzzy logic based ranking mechanism greatly enhances the system functionality by sending top-most relevant documents based on the relevance scores obtained for the term selection methods and increases the document retrieval accuracy by sending alone the top-most relevant documents instead of transmitting all documents back. As a result, data security is increased by reducing the communication and computational overhead. The experimental validations are performed on RFC and FIRE dataset. Through experimental analysis, we prove that our proposed approach is highly secure and efficient as well as exhibits better recall and precision rate in the IR system to deal with the document-retrieval process.

Keywords

Cloud computing Security Fuzzy logic Outsourced data Document retrieval ECC Query expansion Information retrieval system 

Notes

References

  1. 1.
    Smithamol, M. B., & Sridhar, R. (2018). PECS: Privacy enhanced conjunctive search over encrypted data in the cloud supporting parallel search. Computer Communications, 126, 50–63.CrossRefGoogle Scholar
  2. 2.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., et al. (2009). Above the clouds: A Berkeley view of cloud computing. University of California, Berkeley, Technical report, UCBEECS-2009-28.Google Scholar
  3. 3.
    Tahir, S., Steponkus, L., Ruj, S., Rajarajan, M., & Sajjad, A. (2018). A parallelized disjunctive query based searchable encryption scheme for big data. Future Generation Computer Systems.  https://doi.org/10.1016/j.future.2018.05.048.CrossRefGoogle Scholar
  4. 4.
    Diaz, F., & Metzler, D. (2006). Improving the estimation of relevance models using large external corpora. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 154–161). New York: ACM.Google Scholar
  5. 5.
    Leacock, C., & Chodorow, M. (1998). Combining local context and WordNet similarity for word sense identification. WordNet: An Electronic Lexical Database, 49(2), 265–283.Google Scholar
  6. 6.
    Song, D., Wagner, D., & Perrig, A. (2000). Practical techniques for searches on encrypted data. In Proceedings of IEEE symposium on security and privacy’00.Google Scholar
  7. 7.
    Goh, E.-J. (2003). Secure indexes. Cryptology ePrint archive, report 2003/216. http://eprint.iacr.org/.
  8. 8.
    Boneh, D., Crescenzo, G. D., Ostrovsky, R., & Persiano, G. (2004). Public key encryption with keyword search. In Proceedings of EUROCRYP’04, LNCS (Vol. 3027). Berlin: Springer.Google Scholar
  9. 9.
    Chang, Y. C., & Mitzenmacher, M. (2005). Privacy preserving keyword searches on remote encrypted data. In Proceedings of ACNS’05.Google Scholar
  10. 10.
    Curtmola, R., Garay, J. A., Kamara, S., & Ostrovsky, R. (2006). Searchable symmetric encryption: Improved definitions and efficient constructions. In Proceedings of ACM CCS’06.Google Scholar
  11. 11.
    Singhal, A. (2001). Modern information retrieval: A brief overview. IEEE Data Engineering Bulletin, 24(4), 35–43.Google Scholar
  12. 12.
    Pérez-Agüera, J. R., & Araujo, L. (2008). Comparing and combining methods for automatic query expansion. arXiv preprint arXiv:0804.2057.
  13. 13.
    Sundararaj, V. (2018). Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Communications,.  https://doi.org/10.1007/s11277-018-6014-9.CrossRefGoogle Scholar
  14. 14.
    Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers and Security, 77, 277–288.CrossRefGoogle Scholar
  15. 15.
    Guo, Z., Zhang, H., Sun, C., Wen, Q., & Li, W. (2018). Secure multi-keyword ranked search over encrypted cloud data for multiple data owners. Journal of Systems and Software, 137, 380–395.CrossRefGoogle Scholar
  16. 16.
    Tahir, S., Ruj, S., Sajjad, A., & Rajarajan, M. (2018). Fuzzy keywords enabled ranked searchable encryption scheme for a public cloud environment. Computer Communications, 133, 102–114.CrossRefGoogle Scholar
  17. 17.
    Lee, C.-C. (1990). Fuzzy logic in control systems: Fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 404–418.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Khan, Y. D., Ahmad, F., & Khan, S. A. (2014). Content-based image retrieval using extroverted semantics: A probabilistic approach. Neural Computing and Applications, 24(7–8), 1735–1748.CrossRefGoogle Scholar
  19. 19.
    Lee, K. S., & Croft, W. B. (2013). A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback. Information Processing and Management, 49(4), 792–806.CrossRefGoogle Scholar
  20. 20.
    Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Song, D., Wagner, D., & Perrig, A. (2000). Practical techniques for searches on encrypted data. In Proceedings of the IEEE symposium on security and privacy, California (pp. 44–55).Google Scholar
  22. 22.
    Orencik, C., & Savas, E. (2014). An efficient privacy-preserving multi-keyword search over encrypted cloud data with ranking. Parallel and Distributed Databases, 32, 119–160.CrossRefGoogle Scholar
  23. 23.
    Attrapadung, N., & LiBert, B. (2010). Functional encryption for inner product: Achieving constant size cipher text switch adaptive security or support for negation. In P. Nguyen & D. Pointcheval (Eds.), Public Key Cryptography, LNCS (Vol. 6056, pp. 384–402). Berlin: Springer.Google Scholar
  24. 24.
    Ye, Z., He, B., Huang, X., & Lin, H. (2010) Revisiting Rocchio’s relevance feedback algorithm for probabilistic models. In Asia information retrieval symposium (pp. 151–161). Berlin: Springer.Google Scholar
  25. 25.
    Gupta, K., Silakari, S., Gupta, R., & Khan, S. A. (2009). An ethical way for image encryption using ECC. In First international conference on computational intelligence, communication systems and networks (pp. 342–345).Google Scholar
  26. 26.
    Yates, R. B., & Berthier, R. (1999). Modern information retrieval. Boston: Addisson Wesley.Google Scholar
  27. 27.
    Bache, R., Ballie, M., & Crestani, F. (2013). The likelihood property in general retrieval operations. Information Sciences, 234, 97–111.CrossRefGoogle Scholar
  28. 28.
    Furnas, G. W., Landauer, T. K., Gomez, L. M., & Dumais, S. T. (1987). The vocabulary problem in human-system communication. Communications of the ACM, 30(11), 964–971.CrossRefGoogle Scholar
  29. 29.
    Sundararaj, V. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. International Journal of Intelligent Engineering and Systems, 9(3), 117–126.CrossRefGoogle Scholar
  30. 30.
    Sujatha, K., & Shalini Punithavathani, D. (2018). Optimized ensemble decision-based multi-focus imagefusion using binary genetic Grey-Wolf optimizer in camera sensor networks. Multimedia Tools and Applications, 77(2), 1735–1759.CrossRefGoogle Scholar
  31. 31.
    Hu, H., Xu, J., Ren, C., & Choi, B. (2011). Processing private queries over untrusted data cloud through privacy homomorphism. In Proceedings of ICDE.Google Scholar
  32. 32.
    White, R. W., & Marchionini, G. (2007). Examining the effectiveness of real-time query expansion. Information Processing and Management, 43(3), 685–704.CrossRefGoogle Scholar
  33. 33.
    Curtmola, R., Garay, J. A., Kamara, S., & Ostrovsky, R. (2006). Searchable symmetric encryption: Improved definitions and efficient constructions. In Proceedings of ACM CCS.Google Scholar
  34. 34.
    Song, D., Wagner, D., & Perrig, A. (2000) Practical techniques for searches on encrypted data. In Proceedings of IEEE symposium on security and privacy.Google Scholar
  35. 35.
    Boneh, D., Crescenzo, G., Ostrovsky, R., & Persiano, G. (2004). Public-key encryption with keyword search. In Proceedings of Eurocrypt.Google Scholar
  36. 36.
    Wang, C., Cao, N., Li, J., Ren, K., & Lou, W. (2010). Secure ranked keyword search over encrypted cloud data. In Proceedings of ICDCS.Google Scholar
  37. 37.
    Goh, E.-J. (2003). Secure indexes. Technical report 2003/216, cryptology, e Print archive. http://eprint.iacr.org.
  38. 38.
    Curtmola, R., Garay, J. A., Kamara, S., & Ostrovsky, R. (2006). Searchable symmetric encryption: Improved definitions and efficient constructions. In Proceedings of 13th ACM conference on computer and communication security, Alexandaria (pp. 79–88).Google Scholar
  39. 39.
    He, B., Huang, J. X., & Zhou, X. (2011). Modeling term proximity for probabilistic information retrieval models. Information Sciences, 181(14), 3017–3031.MathSciNetCrossRefGoogle Scholar
  40. 40.
    Wang, C., Cao, N., Ren, K., & Lou, W. (2012). Enabling secure and efficient ranked keyword search over outsourced cloud data. IEEE Transactions on Parallel and Distributed Systems, 23(8), 1467–1479.CrossRefGoogle Scholar
  41. 41.
    Pedronette, D. C. G., Almeida, J., & Torres, R. D. S. (2014). A scalable re-ranking method for content-based image retrieval. Information Sciences, 265, 91–104.MathSciNetCrossRefGoogle Scholar
  42. 42.
    Yu, J., Lu, P., Zhu, Y., Xue, G., & Li, M. (2013). Toward secure multikeyword top-k retrieval over encrypted cloud data. IEEE Transactions on Dependable and Secure Computing, 10(4), 239–250.CrossRefGoogle Scholar
  43. 43.
    Wu, M.-S. (2015). Modeling query-document dependencies with topic language models for information retrieval. Information Sciences, 312, 1–12.CrossRefGoogle Scholar
  44. 44.
    Miao, J., Huang, J. X., & Ye, Z. (2012). Proximity-based rocchio’s model for pseudo relevance. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (pp. 535–544). New York: ACM.Google Scholar
  45. 45.
    Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval (Vol. 1, No. 1). Cambridge: Cambridge University Press.Google Scholar
  46. 46.
    Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M. M., & Gatford, M. (1995). Okapi at TREC-3. Nist Special Publication Sp, 109, 109.Google Scholar
  47. 47.
    Rijsbergen, V., & Joost, C. (1977). A theoretical basis for the use of co-occurrence data in information retrieval. Journal of Documentation, 33(2), 106–119.CrossRefGoogle Scholar
  48. 48.
    Witten, I. H., Moffat, A., & Bell, T. C. (1999). Managing gig a bytes: Compressing and indexing documents and images (2nd ed.). San Francisco, CA: Morgan Kaufmann Series.Google Scholar
  49. 49.
    Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.CrossRefGoogle Scholar
  50. 50.
    The MathWorks, Inc. (2004). MATLAB the language of technical computing: Function reference: A-E version 7 (Vol. 1). Natick: The Math-Works, Inc.Google Scholar
  51. 51.
    Singh, J., & Sharan, A. (2015). Relevance feedback based query expansion model using Borda count and semantic similarity approach. Computational Intelligence and Neuroscience, 2015, 96.CrossRefGoogle Scholar
  52. 52.
    Parapar, J., Presedo-Quindimil, M. A., & Barreiro, A. (2014). Score distributions for pseudo relevance feedback. Information Sciences, 273, 171–181.CrossRefGoogle Scholar
  53. 53.
    Akinribido, C. T., Afolabi, B. S., Akhigbe, B. I., & Udo, I. J. (2011). A fuzzy-ontology based information retrieval system for relevant feedback. International Journal of Computer Science, 8(1), 382–389.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of CSESree Sastha Institute of Engineering and TechnologyChennaiIndia
  2. 2.Department of CSEP.S.R. Engineering CollegeSivakasiIndia

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