Reconstructing Positive Surveys from Negative Surveys with Background Knowledge

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9714)


Negative Survey is a promising technique for collecting sensitive data. Using the negative survey, useful aggregate information could be estimated, while protecting personal privacy. Previous work mainly focuses on improving the general model of the negative survey without considering background knowledge. However, in real-world applications, data analysts usually have some background knowledge on the surveys. Therefore, in this paper, for the first time, we study the usage of background knowledge in negative surveys, and propose a method for accurately reconstructing positive surveys with background knowledge. Moreover, we propose a method for evaluating the dependable level of the positive survey reconstructed with background knowledge. Experimental results show that more reasonable and accurate positive surveys could be obtained using our methods.


Privacy protection Sensitive data collection Negative survey Background knowledge 



This work is partly supported by National Natural Science Foundation of China (No. 61175045).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Anhui Province Key Laboratory of Software Engineering in Computing and CommunicationUniversity of Science and Technology of ChinaHefeiChina

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