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A survey: deriving private information from perturbed data

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Abstract

Privacy-preserving data mining has attracted the attention of a large number of researchers. Many data perturbation methods have been proposed to ensure individual privacy. Such methods seem to be successful in providing privacy and accuracy. On one hand, different methods are utilized to preserve privacy. On the other hand, various data reconstruction approaches have been proposed to derive private information from perturbed data. Thus, many researchers have been conducting various studies about data reconstruction methods and the resilience of data perturbation schemes. In this survey, we focus on data reconstruction methods due to their importance in privacy-preserving data mining. We provide a detailed review of the data reconstruction methods and the data perturbation schemes attacked by different data reconstruction techniques. We merge our review with the evaluation metrics and the data sets used in current attack techniques. Finally, we pose some open questions to provide a better understanding of these approaches and to guide future study.

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Acknowledgments

This work is supported by Grant 113E262 from TUBITAK.

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Authors and Affiliations

  1. Computer Engineering Department, Yalova University, 77100, Yalova, Turkey

    Burcu D. Okkalioglu & Murat Okkalioglu

  2. Electrical and Electronics Engineering Department, Bilecik Seyh Edebali University, 11210, Bilecik, Turkey

    Mehmet Koc

  3. Computer Engineering Department, Anadolu University, 26470, Eskisehir, Turkey

    Huseyin Polat

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  1. Burcu D. Okkalioglu
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  2. Murat Okkalioglu
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  3. Mehmet Koc
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  4. Huseyin Polat
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Correspondence to Huseyin Polat.

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Okkalioglu, B.D., Okkalioglu, M., Koc, M. et al. A survey: deriving private information from perturbed data. Artif Intell Rev 44, 547–569 (2015). https://doi.org/10.1007/s10462-015-9439-5

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