Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

Authors

  • S Krishna Kishore
  • Gudipati Murali
  • A Chandra Mouli

DOI:

https://doi.org/10.14419/ijet.v7i3.27.17998

Published:

2018-08-15

Keywords:

Inquiry benefits in the cloud, security, run question, kNN question

Abstract

With the improvement of administrations figuring and distributed computing, it has turned out to be conceivable to outsource extensive databases to database specialist co-ops and let the suppliers keep up the range-inquiry benefit. Nonetheless, a few information may be touchy that the information proprietor does not have any desire to move to the cloud unless the information classification and inquiry security are ensured. We propose the Random Space Encryption (RASP) approach that permits productive range look with more grounded assault versatility than existing proficiency centered methodologies. The arbitrary space irritation (RASP) information annoyance technique to give secure and proficient range question and kNN inquiry administrations for ensured information in the cloud. The RASP information annoyance strategy consolidates arrange protecting encryption, dimensionality development, arbitrary commotion infusion, and irregular projection, to give solid flexibility to assaults on the irritated information and questions. It likewise saves multidimensional reaches, which enables existing ordering systems to be connected to speedup extend question handling. The kNN-R calculation is intended to work with the RASP go inquiry calculation to process the kNNinquiries.

 

References

[1] Xu H, Guo S & Chen K, Building confidential and efficient query services in the cloud with RASP data perturbationâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.26, No.2, (2014).

[2] Chen K, Kavuluru R & Guo S, “RASP: Efficient Multidimensional Range Query on Attack-Resilient Encrypted Databasesâ€, Proc. ACM Conf. Data and Application Security and Privacy, (2011), pp.249- 260.

[3] Agrawal R, Kiernan J, Srikant R & Xu Y, “Order Preserving Encryption for Numeric Dataâ€, Int’l Conf. Management of Data (SIGMOD), (2004).

[4] Armbrust M, Fox A, Griffith R, Joseph AD, Andy Konwinski RK, Lee G, Patterson D, Rabkin A, Stoica I & Zaharia M, “Above the Clouds: A Berkeley View of Cloud Computingâ€, Technical report, Univ. of Berkerley, (2009).

[5] Bau J & Mitchell JC, “Security Modeling and Analysisâ€, IEEE Security and Privacy, Vol.9, No.3, (2011), pp.18-25.

[6] Cao N, Wang C, Li M, Ren K & Lou W, “Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Dataâ€, Proc. IEEE INFOCOMM, (2011).

[7] Chen K & Liu L, “Geometric Data Perturbation for Outsourced Data Miningâ€, Knowledge and Information Systems, Vol.29, (2011), pp.657- 695.

[8] G, Abikhanova, A Ahmetbekova, E Bayat, A Donbaeva, G Burkitbay (2018). International motifs and plots in the Kazakh epics in China (on the materials of the Kazakh epics in China), Opción, Año 33, No. 85. 20-43.

[9] D, Ibrayeva, Z Salkhanova, B Joldasbekova, Zh Bayanbayeva (2018). The specifics of the art autobiography genre. Opción, Año 33. 126-151.

[10] Chen K, Liu L & Sun G, “Towards Attack-Resilient Geometric Data Perturbationâ€, Proc. SIAM Int’l Conf. Data Mining, (2007).

[11] Chor EK, Goldreich O & Sudan M, “Private Information Retrievalâ€, ACM Computer Survey, Vol.45, No.6, (1998), pp.965-981.

[12] Curtmola R, Garay J, Kamara S & Ostrovsky R, “Searchable Symmetric Encryption: Improved Definitions and Efficient Constructionsâ€, 13th ACM Conf. Computer and Comm. Security, (2006), pp.79- 88.

[13] Marimont R & Shapiro M, “Nearest Neighbour Searches and the Curse of Dimensionalityâ€, J. Inst. of Math. and Its Applications, Vol.24, (1979), pp.59-70.

[14] Hacigumus H, Iyer B, Li C & Mehrotra S, “Executing SQL over Encrypted Data in the Database-Service-Provider Modelâ€, ACM SIGMOD Int’l Conf. Management of Data (SIGMOD), (2002).

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