报告题目: Security and Privacy Preservation in Mobile Crowdsensing

报告人：Jianbing Ni 博士，滑铁卢大学，加拿大

时间: 2018年08月14日 14:00-15:00

地点: 文津楼三段 622报告厅

报告摘要: Mobile crowdsensing (MCS) enablesa crowd of individuals having mobile devices capable of sensing and computing collaboratively share data and extract information of common interest. It has become a new business model to individuals and corporations for data collection and analysis, and massive MCS applications have been developed nowadays, such as restaurant recommendation, health status monitoring and customer satisfaction survey. MCS brings a series of competitive advantages compared with mobile sensor networks; on the other hand, it is confronted with various security and privacy threats, which would impede its health development. However, protecting MCS applications against security and privacy risks is extremely challenging due to the diversity of mobile users who participate in MCS for data collection, and the desired functionalities of MCS, in which private information of mobile users is needed to improve data quality. In the presentation, I deal with the security and privacy challenges in MCS and come up with new ideas and novel mechanisms to address these challenges. A secure and privacy-preserving mobile crowdsensing framework is proposed, which provides security protection for MCS applications and privacy preservation for mobile users, while supporting the diversity of mobile users and enabling all the functionalities, including task allocation, data collection, data analysis and reward feedback. Specifically, i) a strong privacy-preserving task allocation scheme is developed to achieve accurate task allocation based on personal information of mobile users, without disclosing their personal information; ii) a secure data deduplication scheme is proposed to allow network nodes to detect and delete replicate data, without exposing data contents to the network nodes; iii) a privacy-preserving data statistics scheme is designed to enable service providers to conduct statistics on the collected data, without having any knowledge about data; and iv) a dual-anonymous reward distribution scheme is proposed to enable service providers to distribute rewards to mobile users based on their contributions, without learning the identities of mobile users. Each proposed scheme realizes a seemingly impossible goal based on novel cryptographic technologies. The research results can be implemented to achieve security and privacy in MCS and enrich the theoretical research of cryptography.

报告人介绍：Jianbing Ni received the Ph. D. degree at Department of Electrical and Computer Engineering, University of Waterloo. He received the B.E. degree on Information Security and the M.E. degree on Computer Applied Technology from the School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu, China, in 2011 and 2014, respectively. His research interests are applied cryptography and network security; especially network where security and privacy are of great concern (e.g., autonomous driving, smart grid, mobile crowdsensing, fog computing and Internet-of-Things (IoT)). He has published over 40 Journal/conference papers, including high-quality journal papers, such as IEEE Transactions on Smart Grid, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, and mainstream conferences, such as Securecomm, IEEE GLOBECOM, IEEE ICC, NSS. He was the recipient of the ICC 2018, Globecom 2017, WCSP 2017, Securecomm 2016, BWCCA 2015 Best Paper Award. He has been serving as many designated professional reviewers of high quality journals, such as IEEE JSAC, TSG, TMC, COMST, TVT and TCC, and TPC member for many conferences, such as ProvSec 2017, IEEE GLOBECOM 2015, 2016, 2017, 2018.