Study on Privacy Preserving and Accuracy of Negative Survey Under Non-Exclusive Choice Selection

Ran Liu, Chi-Hung Chi, Borui Cai, Guangyan Huang, Yankin Chi, Raymond K. Wong, Yanfeng Shu, Jing He

Abstract: Negative survey aims for a cost-effective privacy preserving mechanism for multiple choice question-answering. In most existing work, one important assumption is the exclusivity of answers. However, there are many situations where all the selectable answers are not exclusive - they just have different degrees of an interviewee’s preference. This results in significant distortion of the accuracy of the reconstructed distribution. In this paper, we propose to extend current negative survey models to address surveys with non-exclusive selectable answers. Based on our new model, we investigate the relationship between the accuracy of the reconstructed distribution and the loss of personal privacy in detail. We show that in many cases, the accuracy of the reconstructed distribution can be improved substantially with acceptable slight loss in personal privacy if the interviewee is willing to indicate his preference degree to the negative answer he chooses. Furthermore, when the exclusivity of the positive answer goes below a certain threshold, it is actually possible for both the accuracy and privacy of the survey to be improved together. It is because the selected negative answer with higher preference degree might be more preferable than the positive answer with lower preference degree in the distribution reconstruction.