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Many recent proposals for anonymous communication omit from their security analyses a consideration of the effects of time on important system components. In practice, many components of anonymity systems, such as the client location and network structure, exhibit changes and patterns over time. In this paper, we focus on the effect of such temporal dynamics on the security of anonymity networks. We present Tempest, a suite of novel attacks based on (1) client mobility, (2) usage patterns, and (3) changes in the underlying network routing. Using experimental analysis on real-world datasets, we demonstrate that these temporal attacks degrade user privacy across a wide range of anonymity networks, including deployed systems such as Tor; pathselection protocols for Tor such as DeNASA, TAPS, and Counter-RAPTOR; and network-layer anonymity protocols for Internet routing such as Dovetail and HORNET. The degradation is in some cases surprisingly severe. For example, a single host failure or network route change could quickly and with high certainty identify the client’s ISP to a malicious host or ISP. The adversary behind each attack is relatively weak – generally passive and in control of one network location or a small number of hosts. Our findings suggest that designers of anonymity systems should rigorously consider the impact of temporal dynamics when analyzing anonymity.
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belongs to the medical industry, and temporal features include period of time, day of the week, whether the publishing date is an official holiday, and the development stage of epidemics. As the topical features are seldom investigated in similar studies, content features are also considered and represented by the topics involved in microblog entries.
First, in the category of publishers’ features, the publisher’s type is determined by the certification information and the name of the publisher. According to the Sina Weibo’s certification system, all of the certified
the time of Danmu comments sent by users is actually different ( Johnson, 2013 ). Pseudo-proximity captures the space dimension and has been further classified into spatial proximity and temporal proximity ( Fan et al. 2018 ). Spatial proximity refers to the physical closeness between the Danmu comment area and the focal area of the video section, whereas temporal proximity refers to the temporal closeness between the Danmu and the video ( Fan et al., 2018 ). Comment-content congruency reflects the support dimension and refers to the consistency between the content
Yukun Zheng, Yiqun Liu, Zhen Fan, Cheng Luo, Qingyao Ai, Min Zhang and Shaoping Ma
( Dupret & Piwowarski, 2008 ). Furthermore, Wang et al. (2015) looked into the revisiting behaviors of users in SERPs and incorporated non-sequential behaviors into the PSCM. Liu et al. (2017) proposed the time-aware click model (TACM), which can better capture the temporal information.
2.2 Document Ranking
A lot of learning-to-rank approaches have been proposed to address document ranking problem, such as RankNet ( Burges et al., 2005 ), RankBoost ( Freund et al., 2003 ), and LambdaMART (Wu, Burges, Svore, & Gao, 2010). All these learning-to-rank algorithms
Research and Development in Information Retrieval 595 602 10.1145/1390334.1390436
Efron, M., Lin, J. J., He, J., & de Vries, A. P. (2014). Temporal feedback for tweet search with non-parametric density estimation. International Conference on Research and Development in Information Retrieval , 33–42. Efron M. Lin J. J. He J. de Vries A. P. 2014 Temporal feedback for tweet search with non-parametric density estimation International Conference on Research and Development in Information Retrieval 33 42
Ghosh, S., & Desarkar, M. S. (2018). Class specific TF-IDF boosting