Negative Survey with Manual Selection: A Case Study in Chinese Universities

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10366)


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.


Privacy protection Negative survey Reconstruction method 



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina
  2. 2.School of Computer ScienceChina University of GeosciencesWuhanChina

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