Memetic Algorithm for Solving the Task of Providing Group Anonymity

  • Oleg Chertov
  • Dan Tavrov
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 312)


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.


Local Search Memetic Algorithm Local Search Procedure Singular Spectrum Analysis Heuristic Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Applied Mathematics DepartmentNational Technical University of Ukraine “Kyiv Polytechnic Institute”KyivUkraine

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