報告內容🦵🏿：Study on the Privacy Preserving and Accuracy of Negative Survey Under Non-Exclusive Choice Selection
報告人🙆🏻：支誌雄 教授
報告時間：11月25日 9:00
報告方式：線上（騰訊會議：944 3389 6079）
指導人簡介:
支誌雄🤦🏼♂️，教授🪠，深資首席研究員🏄🏽。目前就職於澳大利亞聯邦科學與工業組織（CSIRO）Data61研究所🕷，任雲計算和傳感數據安全組的科研組長。博士畢業於美國普渡大學西拉法葉分校😗🛡，先後在飛利浦研究實驗室、IBM公司、香港中文大學、新加坡國立大學、清華大學任職🪷，多次擔任WCW , AWCC , IEEE SOSEICBE，lCS0C 等國際會議的會議主席，發表學術論文超過近300篇⛺️，擁有多項已經產業化的美國專利。支教授目前的研究領域包括行為信息學和分析學，網絡安全👱🏿♀️，物聯網，雲計算、服務計算和社交網絡。
報告內容簡介👋🏼：
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.
