Advertisement

Handling Unreasonable Data in Negative Surveys

  • Jianwen Xiang
  • Shu Fang
  • Dongdong ZhaoEmail author
  • Jing Tian
  • Shengwu XiongEmail author
  • Dong Li
  • Chunhui Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

Negative survey is a method of collecting sensitive data. Compared with traditional surveys, negative survey can effectively protect the privacy of participants. Data collector usually has some background knowledge about the survey, and background knowledge could be effectively used for estimating aggregated results from the collected data. Traditional methods for estimating aggregated results would get some unreasonable data, such as negative values, and some values inconsistent with the background knowledge. Handling these unreasonable data could improve the accuracy of the estimated aggregated results. In this paper, we propose a method for handling values that are inconsistent with the background knowledge and negative values. The simulation results show that, compared with NStoPS, NStoPS-I and NStoPS-BK, more accurate aggregated results could be estimated by the proposed method.

Keywords

Negative survey Unreasonable data Background knowledge Aggregated results Data adjustment 

Notes

Acknowledgment

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61672398), the Hubei Provincial Natural Science Foundation of China (Grant No. 2017CFA012), the Key Technical Innovation Project of Hubei (Grant No. 2017AAA122), the Applied Fundamental Research of Wuhan (Grant No. 20160101010004), and the Open Fund of Hubei Key Lab. of Transportation of IoT (Grant No. 2017III028-004).

References

  1. 1.
    Sun, X., Wang, H., Li, J., et al.: Publishing anonymous survey rating data. Data Min. Knowl. Discov. 23(3), 379–406 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Esponda, F., Ackley, E.S., Helman, P., Jia, H., Forrest, S.: Protecting data privacy through hard-to-reverse negative databases. Int. J. Inf. Secur. 6, 403–415 (2007)CrossRefGoogle Scholar
  3. 3.
    Esponda, F.: Everything that is not important: negative databases. IEEE Comput. Intell. Mag. 3, 60–63 (2008)CrossRefGoogle Scholar
  4. 4.
    Liu, R., Luo, W., Yue, L.: Classifying and clustering in negative databases. Front. Comput. Sci. 7(6), 864–874 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Esponda, F.: Negative representations of information. Ph.D. thesis, University of New Mexico (2005)Google Scholar
  6. 6.
    Esponda, F.: Negative surveys (2006). arXiv:math/0608176
  7. 7.
    Esponda, F., Guerrero, V.M.: Surveys with negative questions for sensitive items. Stat. Probab. Lett. 79, 2456–2461 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Horey, J., Groat, M., Forrest, S., Esponda, F.: Anonymous data collection in sensor networks. In: The Fourth Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Philadelphia, USA, pp. 1–8 (2007)Google Scholar
  9. 9.
    Horey, J., Forrest, S., Groat, M.M.: Reconstructing spatial distributions from anonymized locations. In: The 2012 IEEE 28th International Conference on Data Engineering Workshops (ICDEW), Arlington, VA, pp. 243–250 (2012)Google Scholar
  10. 10.
    Luo, W., Lu, Y., Zhao, D., et al.: On location and trace privacy of the moving object using the negative survey. IEEE Trans. Emerg. Top. Comput. Intell. PP(99), 1 (2017)Google Scholar
  11. 11.
    Luo, W., Jiang, H., Zhao, D.: Rating credits of online merchants using negative ranks. IEEE Trans. Emerg. Top. Comput. Intell. 1(5), 354–365 (2017)CrossRefGoogle Scholar
  12. 12.
    Bao, Y., Luo, W., Zhang, X.: Estimating positive surveys from negative surveys. Stat. Probab. Lett. 83, 551–558 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lu, Y., Luo, W., Zhao, D.: Fast searching optimal negative surveys. In: ICINS 2014 - 2014 International Conference on Information and Network Security, p. 27 (2014)Google Scholar
  14. 14.
    Zhao, D., Luo, W., Yue, L.: Reconstructing positive surveys from negative surveys with background knowledge. In: Tan, Y., Shi, Y. (eds.) Data Mining and Big Data. DMBD (2016). LNCS, vol. 9714, pp. 86–99. Springer, Cham.  https://doi.org/10.1007/978-3-319-40973-3_9Google Scholar
  15. 15.
    Esponda, F., Huerta, K., Guerrero, V.M.: A statistical approach to provide individualized privacy for surveys. PLoS ONE 11(1), 1–14 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina
  2. 2.Software Quality Engineering Research CenterCEPREIGuangzhouChina

Personalised recommendations