Preservation of Online Users' Keyword Search Data Using Asymmetric Algorithm Method

Authors

  • R. Anusha  Research Scholar, Department of Computer Science, A.V.C College, Mayiladuthurai, India
  • D. Sudha  Associate Professor, Department of Computer Science, A.V.C College, Mayiladuthurai, India

Keywords:

RSA, Homomorphic, Privacy Preserving, Selective Aggregation

Abstract

Internet is the place where tons of online user behavior data are generated every day. These data are used to extract users' valuable information for research purposes or business interests. But, the data are under the risk of being exposed to third parties. Methods are implemented to perform the data aggregation in a privacy preserving manner. Most of the available methods assure strong privacy protection at the cost of very limited aggregation such as only summation, which hardly satisfies the need of behavior analysis. In this paper, proposed a model called PPSA. This model encrypts the users' sensitive data to prevent privacy from both outside analysts and the aggregation service provider. Also, completely supports selective aggregate functions for online user behavior analysis and guaranteeing differential privacy. Homomorphic RSA algorithm is used for encrypting users’ online behavior data. Implementation is done and its performances are evaluated based on a real time behavior set. Experimental results show that the proposed method effectively supports both overall aggregate queries and various selective aggregate queries with acceptable computation and communication overheads.

References

  1. Jianwei Qian, Fudong Qiu, Student Member, IEEE, Fan Wu, Member, IEEE, Na Ruan, Member,IEEE, Guihai Chen, Member, IEEE, and Shaojie Tang, Member, IEEE, "privacy preserving selective aggregation using onlune user behavior data",2016.
  2. Rastogi and S. Nath, "Differentially private aggregation of distributed time-series with transformation and encryption," in Proceedings of the ACM International Conference on Management of Data (SIGMOD), 2010, pp. 735-746.
  3. Applebaum, H. Ringberg, M. J. Freedman, M. Caesar, and J. Rexford, "Collaborative, privacy-preserving data aggregation at scale," in Proceedings of the 10th Privacy Enhancing Technologies Symposium (PETS), 2010, pp. 56-74.
  4. E. Bucklin and C. Sismeiro, "Click here for internet insight: Advances in click stream data analysis in marketing," Journal of Interactive Marketing, vol. 23, no. 1, pp. 35-48, 2009.
  5. Chen, R. H. Chiang, and V. C. Storey, "Business intelligence and analytics: From big data to big impact." MIS quarterly, vol. 36, no. 4, pp. 1165-1188, 2012.
  6. Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine , Kristin Lauter, Michael Naehrig, John Wernsin, "CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy", 33 rd International Conference on Machine Learning, New York, NY, USA, 2016. Volume 48, pp: 1 - 10.
  7. Ihsan Jabbar, Saad Najim, "Using Fully Homomorphic Encryption to Secure Cloud Computing" Internet of Things and Cloud Computing, Volume 4, Issue 2, April 2016, Pages: 13-18.
  8. Jung Hee Cheon, Andrey Kim, Miran Kim and Yongsoo Song, "Homomorphic Encryption for Arithmetic of Approximate Numbers", International Conference on the Theory and Application of Cryptology and Information Security ASIACRYPT 2017, pp 409-437.
  9. Sungwook Kimy, Jinsu Kimz, Dongyoung Koox, Yuna Kim, Hyunsoo Yoonk and Junbum Shin, "Efficient Privacy-Preserving Matrix Factorization via Fully Homomorphic Encryption" 11th ACM on Asia Conference on Computer and Communications Security, May 2016, pp: 617-628.
  10. Ana Costache and Nigel P. Smart,"Which Ring Based Somewhat Homomorphic Encryption Scheme is Best" CT-RSA 2016: Topics in Cryptology, 2016, pp: 325-340.
  11. Aono, T. Hayashi, L. T. Phong, and L. Wang, "Efficient homomorphic encryption with key rotation and security update," IEICE Trans. Inf. Syst., vol. E101-A, no. 1, pp. 39-50, 2018.
  12. Banaszczyk, "New bounds in some transference theorems in the geometry of numbers," Math. Annalen, vol. 296, no. 1, pp. 625-635, 1993.
  13. Banaszczyk, "Inequalities for convex bodies and polar reciprocal lattices in Rn ," Discrete Comput. Geometry, vol. 13, no. 1, pp. 217-231, 1995.
  14. Bonawitz et al., "Practical secure aggregation for privacy preserving machine learning," in Proc. ACM SIGSAC Conf. Comput. Commun. Secur. (CCS), 2017, pp. 1175-1191.
  15. Chillotti, N. Gama, M. Georgieva, and M. E. Izabachène "Faster fully homomorphic encryption: Bootstrapping in less than 0.1 seconds," in Proc. Adv. Cryptol.-ASIACRYPT, 2016, pp. 3-33.
  16. Dean et al., "Large scale distributed deep networks," in Proc. 26th Annu. Conf. Neural Inf. Process. Syst., 2012, pp. 1232-1240.
  17. Duchi, E. Hazan, and Y. Singer, "Adaptive subgradient methods for online learning and stochastic optimization," J. Mach. Learn. Res., vol. 12, pp. 2121-2159, Feb. 2011.
  18. Gilad-Bachrach et al., "Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy," in Proc. 33nd Int. Conf. Mach. Learn. (ICML), vol. 48. 2016, pp. 201-210.
  19. Goldreich, Foundations of Cryptography: Basic Applications. vol.2. Cambridge, U.K.: Cambridge Univ. Press, 2004.
  20. B. Hitaj, G. Ateniese, and F. Pérez-Cruz, "Deep models under the GAN: Information leakage from collaborative deep learning," in Proc. ACM SIGSAC Conf. Comput. Commun. Secur. (CCS), 2017, pp. 603 - 618.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-10-30

Issue

Volume 3, Issue 7, September-October-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R. Anusha, D. Sudha, " Preservation of Online Users' Keyword Search Data Using Asymmetric Algorithm Method, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.266-275, September-October-2018.