Zhou You, Zhou Yian, Chen Shigang, Wu Samuel S
Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL, USA.
Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA, USA.
Proc Int Conf Distrib Comput Syst. 2017;2017. doi: 10.1109/icdcs.2017.247. Epub 2017 Jul 17.
Thanks to the proliferation of Internet access and modern digital and mobile devices, online survey has been flourishing into data collection of marketing, social, financial and medical studies. However, traditional data collection methods in online survey suffer from serious privacy issues. Existing privacy protection techniques are not adequate for online survey for lack of strong privacy. In this paper, we propose a practical strong privacy online survey scheme SPS based on a novel data collection technique called (DM), which guarantees the correctness of the tallying results with low computation overhead, and achieves universal verifiability, robustness and strong privacy. We also propose a more robust scheme RSPS, which incorporates multiple distributed survey managers. The RSPS scheme preserves the nice properties of SPS, and further achieves robust strong privacy against joint collusion attack. Through extensive analyses, we demonstrate our proposed schemes can be efficiently applied to online survey with accuracy and strong privacy.
得益于互联网接入以及现代数字和移动设备的普及，在线调查已蓬勃发展成为市场营销、社会、金融和医学研究的数据收集方式。然而，在线调查中的传统数据收集方法存在严重的隐私问题。现有的隐私保护技术因缺乏强大的隐私性而不足以用于在线调查。在本文中，我们基于一种名为（DM）的新型数据收集技术提出了一种实用的强隐私在线调查方案SPS，该方案以低计算开销保证了计票结果的正确性，并实现了通用可验证性、鲁棒性和强隐私性。我们还提出了一种更健壮的方案RSPS，它纳入了多个分布式调查管理器。RSPS方案保留了SPS的良好特性，并进一步实现了针对联合勾结攻击的健壮强隐私性。通过广泛的分析，我们证明了我们提出的方案可以准确且强隐私地高效应用于在线调查。
