Encrypted Mobile Cloud Data Searching With Efficient Traffic and Energy Saving Method

Authors

  • M. Jamuna Student, Department of Computer Science and Engineering, School of Engineering and Technology, Sri Padmavati MahilaVisva Vidyalayam, Tirupati, Andhra Pradesh, India
  • A. Supriya Assistant Professor, Department of Computer Science and Engineering, School of Engineering and Technology, Sri Padmavati MahilaVisva Vidyalayam, Tirupati, Andhra Pradesh, India

DOI:

https://doi.org/10.51983/ajcst-2019.8.S3.2049

Keywords:

Mobile Cloud Storage, Searchable Data Encryption, Energy Efficiency, Traffic Efficiency

Abstract

Cloud storage provides a convenient, massive, and scalable storage at low value; however information privacy could be a major concern that prevents users from storing files on the cloud confidingly. a technique of enhancing privacy from information owner purpose of read is to cipher the files before outsourcing them onto the cloud and decode the files when downloading them. However, encryption could be a heavy overhead for the mobile devices, and information retrieval method incurs an advanced communication between the information user and cloud. Commonly with restricted bandwidth capability and restricted battery life, these problems introduce significant overhead to computing and communication as well as the next power consumption for mobile device users that makes the encrypted search over mobile cloud terribly difficult. During this paper, we tend to propose traffic and energy saving encrypted search (TEES), a bandwidth and energy efficient encrypted search design over mobile cloud. The planned design offloads the computation from mobile devices to the cloud, and that we any optimize the communication between the mobile clients and also the cloud. It’s demonstrated that the information privacy doesn’t degrade once the performance sweetening ways square measure applied. Our experiments show that TEES reduces the computation time by twenty three to forty six p.c and save the energy consumption by 35 to 55 percent per file retrieval; meanwhile the network traffics throughout the file retrievals also are considerably reduced.

References

D. Hiemstra, "A probabilistic justification for using tf_idf term weighting in information retrieval," Int. J. Digital Libraries, Vol. 3, No. 2, pp. 131–139, 2000.

L. Vaquero, L. Rodero-Merino, J. Caceres, and M. Lindner, "A break in the clouds: Towards a cloud definition," ACM SIGCOMM Computer Commun. Rev., Vol. 39, No. 1, pp. 50–55, 2008.

D. Huang, "Mobile cloud computing," IEEE COMSOC Multimedia Commun. Techn. Committee E-Letter, Vol. 6, No. 10, pp. 27–31, 2011.

C. Wang, N. Cao, K. Ren, and W. Lou, "Enabling secure and efficient ranked keyword search over outsourced cloud data," IEEE Trans. Parallel Distrib. Syst., Vol. 23, No. 8, pp. 1467–1479, Aug. 2012.

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.

J. Zobel and A. Moffat, "Inverted files for text search engines," ACM Computer. Surveys, Vol. 38, No. 2, pp. 6, 2006.

D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet allocation," J. Mach. Learn. Res., Vol. 3, pp. 993–1022, 2003.

K. Jones, "Index term weighting," Inf. Storage Retrieval, Vol. 9, No. 11, pp. 619–633, 1973.

M. Li, S. Yu, K. Ren, W. Lou, and Y. T. Hou, "Toward privacy assured and searchable cloud data storage services," IEEE Newt., Vol. 27, No. 4, pp. 56–62, Jul./Aug. 2013.

A. Aizawa, "An information-theoretic perspective of tf-idf measures," Inf. Process. Manage, Vol. 39, pp. 45–65, 2003.

K. Kumar and Y. Lu, "Cloud computing for mobile users: Can offloading computation save energy?" Computer, Vol. 43, No. 4, pp. 51–56, 2010.

J. Zobel and A. Moffat, "Exploring the similarity space," ACM SIGIR Forum, Vol. 32, No. 1, pp. 18–34, 1998.

Downloads

Published

05-06-2019

How to Cite

Jamuna, M., & Supriya, A. (2019). Encrypted Mobile Cloud Data Searching With Efficient Traffic and Energy Saving Method. Asian Journal of Computer Science and Technology, 8(S3), 122–127. https://doi.org/10.51983/ajcst-2019.8.S3.2049