Ryan Wails, Yixin Sun, Aaron Johnson, Mung Chiang and Prateek Mittal
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.
Baesens, B., Viaene, S., & Vanthienen, J. (2000) Post-processing of association rules. At The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2000). 20 - 23 Aug 2000.
Bermingham, L., & Lee, I. (2014). Spatio-temporal sequential pattern mining for tourism sciences. Procedia Computer Science , 29, 379-389.
Bhandari, I., Colet, E., Parker, J., Pines, Z., Pratap, R., & Ramanujam, K. K. (1997). Advanced scout: Data mining and knowledge discovery in NBA data. Data Mining and Knowledge Discovery , 1 (1), 121
Thesaurus of Geographic Names adopts W3C PROV to describe revision history of geographic names. W3C PROV is used to document the Activity information about the revision of geographic names, e.g. Activity type (Create, Modify) and temporal information associated with the Activity. Given to the extendibility of W3C PROV, this paper selects W3C PROV to record how metadata vocabularies change as provenance in RDF.
Application of W3C PROV to Metadata Vocabularies
Why Use W3C PROV
The W3C PROV standard includes a set of specifications which refers to many
of the key publications by Stefan W. Hell (in Chinese). Journal of the China Society for Scientific and Technical Information, 34(5), 508–521. Du J. Wu Y. S. 2015 Identifying “sleeping beauties” and the princes who woke up sleeping beauties in terms of citation speed measures: A case study of the key publications by Stefan W. Hell (in Chinese) Journal of the China Society for Scientific and Technical Information 34 5 508 521
Förster, J., Friedman, R. S., & Liberman, N. (2004). Temporal construal effects on abstract and concrete thinking
for data mining to look for international, temporal or disciplinary differences. For example, one study showed that articles tended to be read more often by people from the same country as the authors ( Thelwall & Maflahi, 2015 ).
Large scale altmetric data can be used to assess the validity of specific altmetrics (altmetric) by investigating the extent to which the altmetric correlates with citation counts ( Sud & Thelwall, 2014 ). Although some correlations of this type have already been calculated, it is important to calculate more correlations for different
Research Council (UK), and the Library and Information Commission (UK). Dr. Chen has designed and developed the widely used visual analytics software CiteSpace for visualizing and analyzing structural and temporal patterns in scientific literature.
Science mapping is a generic process of domain analysis and visualization. The scope of a science mapping study can be a scientific discipline, a field of research, or topic areas concerning specific research questions. In other words, the unit of analysis in science mapping is a domain of
/3/DataScienceBook1_1.pdf . Stanton J.M. 2012 Introduction to data science Syracuse University Retrieved on June 6, 2017, from https://ischool.syr.edu/media/documents/2012/3/DataScienceBook1_1.pdf
Sugimoto, S., Li, C., Nagamori, M., & Greenberg, J. (2016). Permanence and temporal interoperability of metadata in the linked open data environment. In Proceedings of the International Conference on Dublin Core and Metadata Applications 2016 (pp. 45–54). Retrieved on June 28, 2017, from http://dcevents.dublincore.org/IntConf/dc-2016/paper/view/430 . Sugimoto S. Li C
Jon Garner, Alan L. Porter, Andreas Leidolf and Michelle Baker
subjects (and for the comparison groups)
Group—benchmarking iUTAH results against two suitable comparison groups.
For the temporal comparisons, we used 2010–2012 as the Before period and 2014–2016 as After. We set aside 2013 as ambiguous with respect to research publications that are apt to reflect participation in the iUTAH project.
Lacking a randomly assigned control group equivalent to iUTAH researchers, we worked to develop reasonable “comparison groups.” Our first comparison group consisted of participants in two Utah-based university centers comparable in
network is known as link prediction ( Liben-Nowell & Kleinberg, 2007 ).
We may distinguish between two types of link prediction applications ( Guns, 2014 ) that have sometimes been confounded in the literature:
Network evolution prediction, and
Network evolution prediction ( Liben-Nowell & Kleinberg, 2007 ) concerns the situation where one is given a temporal snapshot of an evolving network. The task is to predict a future state of the network. Network reconstruction ( Guimerà & Sales-Pardo, 2009 ), on the other hand, concerns the
Kealey T. 1996 The Economic Laws of Scientific Research New York St. Martin’s Press
Kim, H., Yoon, J. W., & Crowcroft, J. (2012). Network analysis of temporal trends in scholarly research productivity. Journal of Informetrics, 6(1), 97−110. 10.1016/j.joi.2011.05.006
Kim H. Yoon J. W. Crowcroft J. 2012 Network analysis of temporal trends in scholarly research productivity Journal of Informetrics 6 1 97 110
King, D. A. (2004). The scientific impact of nations. What different countries get for their research spending. Nature