IEEE INFOCOM 2020
(How Much) Does a Private WAN Improve Cloud Performance?
Todd W Arnold, Ege Gurmericliler and Georgia Essig (Columbia University, USA); Arpit Gupta (Columbia University); Matt Calder (Microsoft); Vasileios Giotsas (Lancaster University, United Kingdom (Great Britain)); Ethan Katz-Bassett (Columbia University, USA)
De-anonymization of Social Networks: the Power of Collectiveness
Jiapeng Zhang and Luoyi Fu (Shanghai Jiao Tong University, China); Xinbing Wang (Shanghai Jiaotong University, China); Songwu Lu (University of California at Los Angeles, USA)
To address this issue, we, for the first time, integrate the multi-hop relationships, which exhibit more structural commonness between networks, into the seedless de-anonymization. We aim to leverage these multi-hop relations to minimize the total disagreements of multi-hop adjacency matrices, which we call collective adjacency disagreements (CADs), between two networks. Theoretically, we demonstrate that CAD enlarges the difference between wrongly and correctly matched node pairs, whereby two networks can be correctly matched w.h.p. even when the network density is below log(n). Algorithmically, we adopt the conditional gradient descending method on a collective-form objective, which efficiently finds the minimal CADs for networks with broad degree distributions. Experiments return desirable accuracies thanks to the rich information manifested by collectiveness since most nodes can be correctly matched when merely utilizing adjacency relations fails to work.
Towards Correlated Queries on Trading of Private Web Browsing History
Hui Cai (Shanghai Jiao Tong University, China); Fan Ye and Yuanyuan Yang (Stony Brook University, USA); Yanmin Zhu (Shanghai Jiao Tong University, China); Jie Li (Shanghai Jiaotong University, China)
Towards Pattern-aware Privacy-preserving Real-time Data Collection
Zhibo Wang, Wenxin Liu and Xiaoyi Pang (Wuhan University, China); Ju Ren (Central South University, China); Zhe Liu (Nanjing University of Aeronautics and Astronautics, China & SnT, University of Luxembourg, Luxembourg); Yongle Chen (Taiyuan University of Technology, China)
In this paper, we propose a novel pattern-aware privacy-preserving approach, called PatternLDP, to protect data privacy while the pattern of time-series can still be preserved. To this end, instead of providing the same level of privacy protection at each data point, each user only samples remarkable points in time-series and adaptively perturbs them according to their impacts on local patterns. In particular, we propose a pattern-aware sampling method to determine whether to sample and perturb current data point, and propose an importance-aware randomization mechanism to adaptively perturb sampled data locally while achieving better trade-off between privacy and utility. Extensive experiments on real-world datasets demonstrate that PatternLDP outperforms existing mechanisms.
Vasanta Chaganti (Swarthmore College)
MagView: A Distributed Magnetic Covert Channel via Video Encoding and Decoding
Juchuan Zhang, Xiaoyu Ji and Wenyuan Xu (Zhejiang University, China); Yi-Chao Chen (Shanghai Jiao Tong University, China); Yuting Tang (University of California, Los Angeles, USA); Gang Qu (University of Maryland, USA)
Stealthy DGoS Attack: DeGrading of Service under the Watch of Network Tomography
Cho-Chun Chiu (The Pennsylvania State University, USA); Ting He (Penn State University, USA)
Voiceprint Mimicry Attack Towards Speaker Verification System in Smart Home
Lei Zhang, Yan Meng, Jiahao Yu, Chong Xiang, Brandon Falk and Haojin Zhu (Shanghai Jiao Tong University, China)
Your Privilege Gives Your Privacy Away: An Analysis of a Home Security Camera Service
Jinyang Li and Zhenyu Li (Institute of Computing Technology, Chinese Academy of Sciences, China); Gareth Tyson (Queen Mary, University of London, United Kingdom (Great Britain)); Gaogang Xie (Institute of Computing Technology, Chinese Academy of Sciences, China)
Qiben Yan (Michigan State University)