Differentially private feature selection under MapReduce framework

doi:10.1016/S1005-8885(13)60094-1

Acta Metallurgica Sinica(English letters) ›› 2013, Vol. 20 ›› Issue (5): 85-90.doi: 10.1016/S1005-8885(13)60094-1

• Wireless • Previous Articles Next Articles

Differentially private feature selection under MapReduce framework

1. College of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China 2. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210023, China 3. Institute of Computer Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Received:2012-12-17 Revised:2013-08-16 Online:2013-10-30 Published:2013-10-29
Contact: Yun LI E-mail:liyun@njupt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61073114, 61105082), the Scientific Research Foundation of Nanjing University of Posts and Telecommunications (NY212020, NY210010) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Abstract

Abstract: Privacy preserving data mining algorithms are crucial for the personal data analysis, such as medical and financial records. This paper focuses on feature selection and proposes a new privacy preserving distributed algorithm, which can effectively select features based on differential privacy and Gini index under the MapReduce framework. At the same time, the theoretic analysis for privacy guarantee is also presented. Some experiments are conducted on bench-mark datasets, the simulation results indicate that during the selection of important features, the proposed algorithm can preserve privacy information to a certain extent with less time cost than on centralized counterpart.

Key words: data mining, feature selection, differential privacy, MapReduce

CHEN Kai WAN Wen-qiang LI Yun. Differentially private feature selection under MapReduce framework[J]. Acta Metallurgica Sinica(English letters), 2013, 20(5): 85-90.

References

1. Dwork C. Differential privacy: a survey of results.Proceedings of the 5th International Conference on Theory and Applications of Models of Computation(TAMC’08), Apr 25-29, 2008, Xi’an, China, 2008: 19p

2. Roy I, Kilzer A, Shmatikov V, et al. Airavat: security and privacy for MapReduce. Proceedings of the 7th USENIX Symposium on Networked Systems Design and Implementation(NSDI’10), Apr 28-30, 2010, San Jose, CA, USA. Berkeley, CA, USA: USENIX Association, 2010: 297-312

3. Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(4): 494-502

4. Zhao Z. Spectral feature selection for mining ultrahigh dimensional data. Tempe, AZ, USA: Arizona State University, 2010

5. Das K. Privacy preserving distributed data mining based on multi-objective optimization and algorithmic game theory. Baltimore, MD,USA: University of Maryland, 2009

6. Gunarathne T, Wu T L, Qiu J, et al. MapReduce in the clouds for science. Proceedings of the 2nd IEEE International Conference on Cloud Computing Technology and Science (CloudCom’10), Nov 30-Dec 3, 2010. Indianapolis, IA, USA. Piscataway, NJ, USA: IEEE, 2010: 565-572

7. Das K, Bhaduri K, Kargupta H. A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks. Knowledge Information System Journal, 2010, 24(3): 341-367

8. HAY M G. Enable accurate analysis of private network data. Amherst, MA, USA: University of Massachusetts, 2010

9. Barak B, Chaudhuri K, Dwork C, et al. Privacy, accuracy, and consistency too: a holistic solution to contingency table release. Proceedings of the Symposium on Principles of Database Systems (PODS’07), Jun 11-13, 2007, Beijing, China. 2007: 273-282

10. Dwork C, Mcsherry F, Nissim K, et al. Calibrating noise to sensitivity in private data analysis. Proceedings of the 3rd Conference on Theory of Cryptography (TCC’06), Mar 4-7, 2006, New York, NY, USA. New York, NY, USA: ACM, 2006: 265-284

11. Mcsherry F, Talwar K. Mechanism design via differential privacy. Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS’07), Oct 20-23, 2007, Providence, Rhode Island, USA. Piscataway, NJ, USA: IEEE, 2007: 94-103

Metrics

Comments

Copyright © 2020 The Journal of China Universities of Posts and Telecommunications
　 Adress: P.O. Box 231,Beijing University of Posts and Telecommunications,10 Xi Tucheng Road,Beijing 100876,P.R.China　Post Code: 100081
Tel：86-010-62282493　Fax： 86-010-62283461　E-mail: jchupt@bupt.edu.cn
Support by: Beijing Magtech Co.Ltd

Differentially private feature selection under MapReduce framework

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 0

Recommended Articles

Metrics

Comments