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The Role of Data Mining in Information Security

Osman Mohamed Abbas, Mohamed Elhafiz Mustafa, Siddig Balal Ibrahim

Abstract


Security and Privacy protection have been a public policy concern for decades. However, rapid technological changes, the rapid growth of the internet and electronic commerce, and the development of more sophisticated methods of collecting, analyzing, and using personal information have made privacy a major public and government issues. The field of data mining is gaining significance recognition to the availability of large amounts of data, easily collected and stored via computer systems.  Recently, the large amount of data, gathered from various channels, contains much personal information. When personal and sensitive data are published and/or analyzed, one important question to take into account is whether the analysis violates the privacy of individuals whose data is referred to. The importance of information that can be used to increase revenue cuts costs or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data privacy is growing constantly. For this reason, many research works have focused on privacy-preserving data mining, proposing novel techniques that allow extracting knowledge while trying to protect the privacy of users. Some of these approaches aim at individual privacy while others aim at corporate privacy [1].  


Keywords


Data Mining; Information security; Threats; Privacy.

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References


Z. Ferdousi, A. Maeda, “Unsupervised outlier detection in time series data”, 22nd International Conference on Data Engineering Workshops, pp. 51-56, 2006

S. A. Demurjian and J. E. Dobson, “Database Security IX Status and Prospects Edited by D. L. Spooner ISBN 0 412 72920 2, 1996, pp. 391- 399.

L. Getoor, C. P. Diehl. “Link mining: a survey”, ACM SIGKDD Explorations, vol. 7, pp. 3-12, 2005.

J. Han, M. Kamber, and J. Pei, Data mining: concepts and techniques. Morgan kaufmann, 2006.

R. Agrawal and R. Srikant, “Privacy-preserving data mining,”SIGMOD Rec., vol. 29, no. 2, pp. 439– 450, 2000.

Y. Lindell and B. Pinkas,“Privacy preserving data mining,”in Advances in Cryptology CRYPTO 2000. Springer, 2000, pp. 36–54.

P. Bergeron, C. A. Hiller, (2002), “Competitive intelligence”, in B.Cronin, Annual Review of nformation Science and Technology,zedford, N.J.: Information Today, vol. 36, chapter 8

V. Ciriani, S. D. C. Di Vimercati, S. Forest, and P. Samarati,“Microdata protection,”in Secure Data Management in Decentralized Systems. Springer, 2007, pp. 291–321.

R. T. Fielding and D. Singer, “Tracking preference expression (dnt).w3c working draft,” 2014.[Online]. Available: http ://www.w3.org/TR/2014/WD- tracking-dnt– 20140128

D. C. Parkes, “Classic mechanism design,” Iterative Combinatorial Auctions: Achieving Economic and Computational Efficiency. Ph. D. dissertation, University of Pennsylvania, 2001.

B. Fung, K. Wang, R. Chen, and P. S. Yu, “Privacy-preservingdata publishing: A survey of recent developments,”ACM Computing Surveys (CSUR), vol. 42, no. 4, p. 14, 2010.

L. Moreau,“The foundations for provenance on the web,” Foundations and Trends in Web Science, vol. 2, no. 2–3, pp. 99–241, 2010.

G. Barbier, Z. Feng, P. Gundecha, and H. Liu, “Provenance data in social media,” Synthesis Lectures on Data Mining and Knowledge Discovery, vol. 4, no. 1, pp. 1–84, 2013.

M. Tudjman and N. Mikelic, “Information science: Science about information, misinformation and disinformation,”Proceedings of Informing Science+ Information Technology Education, pp. 1513–1527, 2003.

M. J. Metzger,“Making sense of credibility on the web: Models for evaluating online information and recommendationsfor futureresearch,”Journal of the American Society for Information Science and Technology, vol. 58, no. 13, pp. 2078–2091, 2007.

J. Vaidya, H. Yu, and X. Jiang, “Privacy- preserving svm classification,”Knowledge and Information Systems, vol. 14, no. 2, pp. 161–178, 2008.

Z. Ferdousi, A. Maeda, “Unsupervised outlier detection in time series data”, 22nd International Conference on Data Engineering Workshops, pp. 51-56, 2006

Larose, D. T., “Discovering Knowledge in Data: An Introduction to Data Mining”, ISBN 0-471-66657-2, ohn Wiley & Sons, Inc, 2005.

Fung B., Wang K., Yu P. ”Top-Down Specialization for Information and Privacy Preservation. ICDE Conference, 2005.

Dunham, M. H., Sridhar S., “Data Mining: Introductory and Advanced Topics”, Pearson Education, New Delhi, ISBN: 81-7758-785-4, 1st Edition, 2006

O. Tene and J. Polenetsky, ‘‘To track or ‘do not track’: Advancing transparency and individual control in online behavioral advertising,’’ Minnesota J. Law, Sci. Technol., no. 1, pp. 281–357, 2012.

R. C.-W. Wong and A. W.-C. Fu, ‘‘Privacy-preserving data publish- ing: An overview,’’ Synthesis Lectures Data Manage., vol. 2, no. 1, pp. 1–138, 2010.

Y. Xua, X. Qin, Z. Yang, Y. Yang, and K. Li, ‘‘A personalized k-anonymity privacy preserving method,’’ J. Inf. Comput. Sci., vol. 10, no. 1, pp. 139–155, 2013.

S. Yang, L. Lijie, Z. Jianpei, and Y. Jing, ‘‘Method for individualized privacy preservation,’’ Int. J. Secur. Appl., vol. 7, no. 6, p. 109, 2013. [52] A. Halevy, A. Rajaraman, and J. Ordille, ‘‘Data integration: The teenage years,’’ in Proc. 32nd Int. Conf. Very Large Data Bases (VLDB), 2006, pp. 9–16.

pp. 9–16. [53] V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin, and Y. Theodoridis, ‘‘State-of-the-art in privacy preserving data mining,’’ ACM SIGMOD Rec., vol. 33, no. 1, pp. 50–57, 2004.

V. S. Verykios, ‘‘Association rule hiding methods,’’ Wiley Interdiscipl. Rev., Data Mining Knowl. Discovery, vol. 3, no. 1, pp. 28–36, 2013.

K. Sathiyapriya and G. S. Sadasivam, ‘‘A survey on privacy preserving association rule mining,’’ Int. J. Data Mining Knowl. Manage. Process, vol. 3, no. 2, p. 119, 2013.

R. K. Adl, M. Askari, K. Barker, and R. Safavi-Naini, ‘‘Privacy consensus in anonymization systems via game theory,’’ in Proc. 26th Annu. Data Appl. Security Privacy, 2012, pp. 74–89.

R. Karimi Adl, K. Barker, and J. Denzinger, ‘‘A negotiation game: Establishing stable privacy policies for aggregate reasoning,’’ Dept. Comput. Sci., Univ. Calgary, Calgary, AB, Canada, Tech. Rep., Oct. 2012. [Online]. Available: The paper is available at http:// dspace.ucalgary.ca/jspui/bitstream/1880/49282/1/2012-1023-06.pdf

A. Miyaji and M. S. Rahman, ‘‘Privacy-preserving data mining: A game-theoretic approach,’’ in Proc. 25th Data Appl. Security Privacy, 2011, pp. 186–200.

X. Ge, L. Yan, J. Zhu, and W. Shi, ‘‘Privacy-preserving distributed association rule mining based on the secret sharing technique,’’ in Proc. 2nd Int. Conf. Softw. Eng. Data Mining (SEDM), Jun. 2010, pp. 345–350.

A. Narayanan and V. Shmatikov, ‘‘Robust de-anonymization of large sparse datasets,’’ in Proc. IEEE Symp. Secur. Privacy (SP), May 2008, pp. 111–125.

B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu, ‘‘Privacy-preserving data publishing: A survey of recent developments,’’ ACM Comput. Surv., vol. 42, no. 4, Jun. 2010, Art. ID 14.

R. C.-W. Wong and A. W.-C. Fu, ‘‘Privacy-preserving data publish- ing: An overview,’’ Synthesis Lectures Data Manage., vol. 2, no. 1, pp. 1–138, 2010.

C. Dwork, ‘‘Differential privacy,’’ in Proc. 33rd Int. Conf. Autom., Lang., Program., 2006, pp. 1–12.

H. Xia, Y. Fu, J. Zhou, and Y. Fang, ‘‘Privacy-preserving SVM classifier with hyperbolic tangent kernel,’’ J. Comput. Inf. Syst., vol. 6, no. 5, pp. 1415–1420, 2010.

S. Jha, L. Kruger, and P. McDaniel, ‘‘Privacy preserving clustering,’’ in Proc. 10th Eur. Symp. Res. Comput. Security (ESORICS), 2005, pp. 397–417.

L. K. Fleischer and Y.-H. Lyu, ‘‘Approximately optimal auctions for selling privacy when costs are correlated with data,’’ in Proc. 13th ACM Conf. Electron. Commerce, 2012, pp. 568–585.

H. Kargupta, K. Das, and K. Liu, ‘‘Multi-party, privacy-preserving distributed data mining using a game theoretic framework,’’ in Proc. 11th Eur. Conf. Principles Pract. Knowl. Discovery Databases (PKDD), 2007, pp. 523–531.

R. K. Adl, M. Askari, K. Barker, and R. Safavi-Naini, ‘‘Privacy consensus in anonymization systems via game theory,’’ in Proc. 26th Annu. Data Appl. Security Privacy, 2012, pp. 74–89.


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