78,433 research outputs found
Measuring and mitigating AS-level adversaries against Tor
The popularity of Tor as an anonymity system has made it a popular target for
a variety of attacks. We focus on traffic correlation attacks, which are no
longer solely in the realm of academic research with recent revelations about
the NSA and GCHQ actively working to implement them in practice.
Our first contribution is an empirical study that allows us to gain a high
fidelity snapshot of the threat of traffic correlation attacks in the wild. We
find that up to 40% of all circuits created by Tor are vulnerable to attacks by
traffic correlation from Autonomous System (AS)-level adversaries, 42% from
colluding AS-level adversaries, and 85% from state-level adversaries. In
addition, we find that in some regions (notably, China and Iran) there exist
many cases where over 95% of all possible circuits are vulnerable to
correlation attacks, emphasizing the need for AS-aware relay-selection.
To mitigate the threat of such attacks, we build Astoria--an AS-aware Tor
client. Astoria leverages recent developments in network measurement to perform
path-prediction and intelligent relay selection. Astoria reduces the number of
vulnerable circuits to 2% against AS-level adversaries, under 5% against
colluding AS-level adversaries, and 25% against state-level adversaries. In
addition, Astoria load balances across the Tor network so as to not overload
any set of relays.Comment: Appearing at NDSS 201
Compositional Vector Space Models for Knowledge Base Completion
Knowledge base (KB) completion adds new facts to a KB by making inferences
from existing facts, for example by inferring with high likelihood
nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop
relational synonyms like this, or use as evidence a multi-hop relational path
treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper
presents an approach that reasons about conjunctions of multi-hop relations
non-atomically, composing the implications of a path using a recursive neural
network (RNN) that takes as inputs vector embeddings of the binary relation in
the path. Not only does this allow us to generalize to paths unseen at training
time, but also, with a single high-capacity RNN, to predict new relation types
not seen when the compositional model was trained (zero-shot learning). We
assemble a new dataset of over 52M relational triples, and show that our method
improves over a traditional classifier by 11%, and a method leveraging
pre-trained embeddings by 7%.Comment: The 53rd Annual Meeting of the Association for Computational
Linguistics and The 7th International Joint Conference of the Asian
Federation of Natural Language Processing, 201
A Social Network Analysis of Occupational Segregation
We develop a social network model of occupational segregation between different social groups, generated by the existence of positive inbreeding bias among individuals from the same group. If network referrals are important in getting a job, then expected inbreeding bias in the contact network structure induces different career choices for individuals from different social groups. This further translates into stable occupational segregation equilibria in the labour market. We derive the conditions for persistent wage and unemployment inequality in the segregation equilibria. Our framework is proposed as complementary to existing theories used to explain labour market inequalities between groups divided by race, ethnicity or genderSocial Networks; Inbreeding Bias; Occupational Segregation; Labour Market Inequality
Conference on Corporate Governance: Search for the Advanced Practices
The purpose of the article is to examine the role of the board of directors in corporate law in Jordan
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