Abstract
Modern information technologies enable us to analyze great amounts of primary non-aggregated data. Publishing them increases threats of disclosing sensitive information. To protect information about a single person, one needs to provide individual data anonymity. Providing group data anonymity presupposes protecting intrinsic data features, properties, and distributions. Methods for providing group anonymity need to protect the underlying data distribution, and also to ensure sufficient data utility after their transformation. In our opinion, the latter task is a problem which can be solved using only exhaustive search, therefore heuristic procedures need to be developed to find suboptimal solutions.
Evolutionary algorithms are heuristic guided random search techniques mimicking biological evolution by natural selection. They are inherently stochastic, which turns out to be a downside when converging to an optimum. Memetic algorithms are a combination of evolutionary algorithms and local search procedures. Applying local search increases convergence and enhances algorithm performance by incorporating problem specific knowledge.
In the paper, we introduce a memetic algorithm for providing group anonymity. We illustrate its application by solving a real data based problem of protecting military personnel regional distribution.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Gantz, J., Reinsel, D.: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East (2012), http://www.emc.com/leadership/digital-universe/iview/executive-summary-a-universe-of.htm
Pfitzmann, A., Hansen, M.: A Terminology for Talking about Privacy by Data Minimization: Anonymity, Unlinkability, Undetectability, Unobservability, Pseudonymity, and Identity Management, Version v0.34 (2010), http://dud.inf.tu-dresden.de/Anon_Terminology.shtml
Chertov, O., Pilipyuk, A.: Statistical Disclosure Control Methods for Microdata. In: 2009 International Symposium on Computing, Communication, and Control (ISCCC 2009). Proc. of CSIT, vol. 1, pp. 339–343. IACSIT Press, Singapore (2011)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2007)
Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Toward Memetic Algorithms. C3P Report 826: Caltech Concurrent Computation Program, pp. 33–48. Caltech, CA (1989)
Dawkins, R.: The Selfish Gene: 30th Anniversary Edition. Oxford University Press, Oxford (2006)
Neri, F., Cotta, C.: A Primer on Memetic Algorithms. In: Neri, F., Cotta, C., Moscato, P. (eds.) Handbook of Memetic Algorithms. SCI, vol. 379, pp. 43–52. Springer, Heidelberg (2012)
Chertov, O., Tavrov, D.: Data Group Anonymity: General Approach. International Journal of Computer Science and Information Security 8(7), 1–8 (2010)
Chertov, O., Tavrov, D.: Group Anonymity. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. CCIS, vol. 81, pp. 592–601. Springer, Heidelberg (2010)
Domingo-Ferrer, J., Tora, V.: Disclosure Control Methods and Information Loss for Microdata, Confidentiality, Disclosure, and Data Access. In: Doyle, P., Lane, J.I., Theeuwes, J.J.M., Zayatz, L.V. (eds.) Theory and Practical Applications for Statistical Agencies, pp. 91–110. Elsevier, Amsterdam (2001)
Mateo-Sanz, J.M., Domingo-Ferrer, J., Sebé, F.: Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata. Data Mining and Knowledge Discovery 11, 181–193 (2005)
Yancey, W.E., Winkler, W.E., Creecy, R.H.: Disclosure Risk Assessment in Perturbative Microdata Protection. In: Domingo-Ferrer, J. (ed.) Inference Control in Statistical Databases. LNCS, vol. 2316, pp. 135–152. Springer, Heidelberg (2002)
Chertov, O. (ed.): Group Methods of Data Processing. Lulu.com, Raleigh (2010)
Liu, L., Wang, J., Zhang, J.: Wavelet-Based Data Perturbation for Simultaneous Privacy-Preserving and Statistics-Preserving. In: 2008 IEEE International Conference on Data Mining Workshops, Pisa, pp. 27–35. IEEE Computer Society Press (2008)
Chertov, O., Tavrov, D.: Providing Group Anonymity Using Wavelet Transform. In: MacKinnon, L.M. (ed.) BNCOD 2010. LNCS, vol. 6121, pp. 25–36. Springer, Heidelberg (2012)
Tavrov, D., Chertov, O.: SSA-Caterpillar in Group Anonymity. In: Paper presented at the World Conference on Soft Computing, May 23-26. San Francisco State University, San Francisco (2011)
Chertov, O.R.: Minimizatsiia spotvoren pry formuvanni mikrofailu z zamaskovanymy danymy (In Ukrainian). Visnyk Skhidnoukrainskoho Natsionalnoho Universytetu imeni Volodymyra Dalia 8(179), 256–262 (2012)
Chertov, O.R., Tavrov, D.Y.: Memetychnyi alhorytm dlia modyfikatsii mikrofailu z minimizatsiieiu spotvoren u protsesi zabezpechennia hrupovoi anonimnosti. Shtuchnyi Intelekt (in press, 2013) (in Ukrainian)
Smith, A.E., Coit, D.W.: Penalty Functions. In: Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.) Evolutionary Computation 2. Advanced Algorithms and Operators, pp. 41–48. Institute of Physics Publishing, Bristol (2000)
Goldberg, D.E., Korb, B., Deb, K.: Messy Genetic Algorithms: Motivation, Analysis, and First Results. Complex Systems 3, 493–530 (1989)
Brindle, A.: Genetic Algorithms for Function Optimization. Doctoral dissertation and technical report TR81-2, University of Alberta, Department of Computer Science (1981)
U.S. Census 2000. 5-Percent Public Use Microdata Sample Files, http://www.census.gov/main/www/cen2000.html
Syswerda, G.: Schedule Optimization Using Genetic Algorithms. In: Davis, L. (ed.) Handbook of Genetic Algorithms, pp. 332–349. Van Nostrand Reinhold, New York (1991)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chertov, O., Tavrov, D. (2014). Memetic Algorithm for Solving the Task of Providing Group Anonymity. In: Jamshidi, M., Kreinovich, V., Kacprzyk, J. (eds) Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-03674-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-03674-8_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03673-1
Online ISBN: 978-3-319-03674-8
eBook Packages: EngineeringEngineering (R0)