报告内容：Study on the Privacy Preserving and Accuracy of Negative Survey Under Non-Exclusive Choice Selection
报告人：支志雄 教授
报告时间：11月25日 9:00
报告方式：线上（腾讯会议：944 3389 6079）
指导人简介:
支志雄，教授，深资首席研究员。目前就职于澳大利亚联邦科学与工业组织（CSIRO）Data61研究所，任云计算和传感数据安全组的科研组长。博士毕业于美国普渡大学西拉法叶分校，先后在飞利浦研究实验室、IBM公司、香港中文大学、新加坡国立大学、清华大学任职，多次担任WCW , AWCC , IEEE SOSEICBE，lCS0C 等国际会议的会议主席，发表学术论文超过近300篇，拥有多项已经产业化的美国专利。支教授目前的研究领域包括行为信息学和分析学，网络安全，物联网，云计算、服务计算和社交网络。
报告内容简介：
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 choices. 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 seminar, 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 details. 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 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.
