Abstract
Negative survey is a promising method which can protect personal privacy while collecting sensitive data. Most of previous works focus on negative survey models with specific hypothesis, e.g., the probability of selecting negative categories follows the uniform distribution or Gaussian distribution. Moreover, as far as we know, negative survey is never conducted with manual selection in real world. In this paper, we carry out such a negative survey and find that the survey may not follow the previous hypothesis. And existing reconstruction methods like NStoPS and NStoPS-I perform poorly on the survey data. Therefore, we propose a method called NStoPS-MLE, which is based on the maximum likelihood estimation, for reconstructing useful information from the collected data. This method also uses background knowledge to enhance its performance. Experimental results show that our method can get more accurate aggregated results than previous methods.
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References
Esponda, F.: Negative surveys. arXiv:math/0608176 (2006)
Esponda, F., Guerrero, V.M.: Surveys with negative questions for sensitive items. Stat. Probab. Lett. 79, 2456–2461 (2009)
Esponda, F., Kael, H., Victor, M.G.: A statistical approach to provide individualized privacy for surveys. PLoS ONE 11(1), e0147314 (2016)
Xie, H., Kulik, L., Tanin, E.: Privacy-aware collection of aggregate spatial data. Data Knowl. Eng. 70, 576–595 (2011)
Bao, Y., Luo, W., Zhang, X.: Estimating positive surveys from negative surveys. Stat. Probab. Lett. 83, 551–558 (2013)
Zhao, D., Luo, W., Yue, L.: Reconstructing positive surveys from negative surveys with background knowledge. In: The 2016 International Conference on Data Mining and Big Data, pp. 86–99 (2016)
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, pp. 1–8 (2007)
Horey, J., Forrest, S., Groat, M.M.: Reconstructing spatial distributions from anonymized locations. In: The 28th International Workshop on Data Engineering, pp. 243–250 (2012)
Liu, R., Tang, S.: Negative survey-based privacy protection of cloud data. In: The 2015 International Conference in Swarm Intelligence, pp. 151–159 (2015)
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (No. 61672398), the Key Natural Science Foundation of Hubei Province of China (No. 2015CFA069), the Applied Fundamental Research of Wuhan (No. 20160101010004), and the Fundamental Research Funds for the Central Universities (No. 173110002).
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Wu, J., Xiang, J., Zhao, D., Li, H., Xie, Q., Hu, X. (2017). Negative Survey with Manual Selection: A Case Study in Chinese Universities. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_5
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DOI: https://doi.org/10.1007/978-3-319-63579-8_5
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