The week's pick
Random Articles
Reseach Article
TRI-Patternization on Generic Visualized Time Series Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 94 - Number 20 |
| Year of Publication: 2014 |
| Authors: Sathiya M, Kumaresan A, Vasanthraj K |
10.5120/16554-6101
|
Sathiya M, Kumaresan A, Vasanthraj K . TRI-Patternization on Generic Visualized Time Series Data. International Journal of Computer Applications. 94, 20 ( May 2014), 34-39. DOI=10.5120/16554-6101
Abstract
In the recent years, Privacy preserving techniques have been actively studied in the time-series data in various fields like financial, medical, and weather analysis. We are focusing towards preserving the data through Anonymity and Generalization technique. We first investigate, what's the privacy to be incorporated at the time-series data and after finding the data which need to be preserved various perturbation terminologies were identified and worked out towards secure multi-party computation (SMC) and encryption techniques in the distributed computing. Our project focused towards Generalized technique in which the data will be filtered or generalized in a grouped structure based on time series grouping algorithm and the data will be shown in the approximation format. So that, the data won't get disclosed. The second technique involves the display of data in the graphical format providing no clue about the exact data and approximation technique incorporates an exact preserving of data. The third technique involves the arrangement of data in the binary tree pattern and this provides an efficient way of ordering the data on the performance basis. The proposed system incorporates all the necessary features, In addition we are trying to incorporate security by adding a deformable/detectable noise to this time series data.
References
- G. Bhargava, P. Goel, and B. R. Iyer. Hypergraph based reorderings of outer join queries with complex predicates. In ACM SIGMOD Conference, pages 304–315, 1995.
- J. A. Blakeley, V. Rao, I. Kunen, A. Prout, M. Henaire, and C. Kleinerman. . NET database programmability and extensibility in Microsoft SQL Server. In Proc. ACM SIGMOD Conference, pages 1087–1098, 2008.
- J. Clear, D. Dunn, B. Harvey, M. L. Heytens, and P. Lohman. Non-stop SQL/MX primitives for knowledge discovery. In ACM KDD Conference, pages 425–429, 1999.
- E. F. Codd. Extending the database relational model to capture more meaning. ACM TODS, 4(4):397–434, 1979.
- C. Cunningham, G. Graefe, and C. A. Galindo-Legaria. PIVOT and UNPIVOT: Optimization and execution strategies in an RDBMS. In Proc. VLDB Conference, pages 998–1009, 2004.
- C. Galindo-Legaria and A. Rosenthal. Outer join simplification and reordering for query optimization. ACM TODS, 22(1):43–73, 1997.
- H. Garcia-Molina, J. D. Ullman, and J. Widom. Database Systems: The Complete Book. Prentice Hall, 1st edition, 2001.
- G. Graefe, U. Fayyad, and S. Chaudhuri. On the efficient gathering of sufficient statistics for classification from large SQL databases. In Proc. ACM KDD Conference, pages 204–208, 1998.
- C. C. Aggarwal and P. S. Yu, "A Condensation Approach to Privacy Preserving Data Mining," Proc. Ninth Int'l Conf. Extending Database Technology (EDBT), pp. 183-199, 2004.
- R. Dewri, I. Ray, and D. Whitley, "On the Optimal Selection of k in the k-Anonymity Problem," Proc. IEEE 24th Int'l Conf. Data Eng. (ICDE), pp. 1364-1366, 2008.
- D. Gunopulos and G. Das, "Time Series Similarity Measures,"Proc. Tutorial Notes of the Sixth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (Tutorial PM-2), pp. 243-307, 2000.
- O. Abul, M. Atzori, F. Bonchi, and F. Giannotti, "Hiding Sequences," Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE) Workshops, pp. 147-156, 2007.
- S. Papadimitriou, F. Li, G. Kollios, and P. S. Yu, "Time Series Compressibility and Privacy," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB), pp. 459-470, 2007.
- Lidan Shou, Xuan Shang, Ke Chen, And Chao Zhang, " Supporting Pattern-Preserving Anonymization For Time-Series Data Proc. Ieee 25th Computer Society,Pp. 1041-4347,2013.
- Yehuda Lindell,and Benny Pinkas, "Secure Multiparty Computation for Privacy-Preserving Data Mining" The Journal of Privacy and Confidentiality , pp. 59-98, 2009.
- L. Sweeney, "k-Anonymity: Privacy Protection Using Generalization and Suppression," Int'l J. Uncertainty Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 571-588, 2002.
- Tutorial on Secure Multi-Party Computation. Yehuda Lindell. IBM T. J. Watson. Outline. Part 1: A rigorous approach to security; Defining security.
- "Graphical and Tabular Summarization of Data", www. utdallas. edu/~scniu/OPRE6301. . /Graphical_and_Tabular. pd.
- M. Balamurugan, S. Chenthur Pandian and J. Bhuvana, "Privacy Preservation for File Sharing Scheme Using Secured File Block id with Binary Trees", American Journal of Applied Sciences, ISSN: 1546-9239, 2013 (http://www. thescipub. com/ajas. toc).
- Likun Liua, liang Hub, DiWangc, Yanmei Huod, Lei Yange, Kexin Yang, " Two Noise Addition Methods For Privacy-Preserving Data Mining", I. J. Wireless and Microwave Technologies, pp. 28-33,2012.
- Michael Dairyko,Lara Pudwell,Samantha Tyner,Casey Wynn," Non-Contiguous Pattern Avoidance in Binary Trees", Published: Aug 23, 2012,Mathematics Subject Classifications: 05C30,05.
Index Terms
Keywords