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.
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 subscriptionsReferences
Sun, X., Wang, H., Li, J., et al.: Publishing anonymous survey rating data. Data Min. Knowl. Discov. 23(3), 379–406 (2011)
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)
Esponda, F.: Everything that is not important: negative databases. IEEE Comput. Intell. Mag. 3, 60–63 (2008)
Liu, R., Luo, W., Yue, L.: Classifying and clustering in negative databases. Front. Comput. Sci. 7(6), 864–874 (2013)
Esponda, F.: Negative representations of information. Ph.D. thesis, University of New Mexico (2005)
Esponda, F.: Negative surveys (2006). arXiv:math/0608176
Esponda, F., Guerrero, V.M.: Surveys with negative questions for sensitive items. Stat. Probab. Lett. 79, 2456–2461 (2009)
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)
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)
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)
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)
Bao, Y., Luo, W., Zhang, X.: Estimating positive surveys from negative surveys. Stat. Probab. Lett. 83, 551–558 (2013)
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)
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_9
Esponda, F., Huerta, K., Guerrero, V.M.: A statistical approach to provide individualized privacy for surveys. PLoS ONE 11(1), 1–14 (2016)
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).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Xiang, J. et al. (2018). Handling Unreasonable Data in Negative Surveys. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-91458-9_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91457-2
Online ISBN: 978-3-319-91458-9
eBook Packages: Computer ScienceComputer Science (R0)