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» Extracting All the Randomness from a Weakly Random Source
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EMNLP
2008
13 years 6 months ago
Weakly-Supervised Acquisition of Labeled Class Instances using Graph Random Walks
We present a graph-based semi-supervised label propagation algorithm for acquiring opendomain labeled classes and their instances from a combination of unstructured and structured...
Partha Pratim Talukdar, Joseph Reisinger, Marius P...
FOCS
2000
IEEE
13 years 9 months ago
Extracting Randomness via Repeated Condensing
Extractors (defined by Nisan and Zuckerman) are procedures that use a small number of truly random bits (called the seed) to extract many (almost) truly random bits from arbitrar...
Omer Reingold, Ronen Shaltiel, Avi Wigderson
TCC
2009
Springer
141views Cryptology» more  TCC 2009»
14 years 6 months ago
Weak Verifiable Random Functions
Verifiable random functions (VRFs), introduced by Micali, Rabin and Vadhan, are pseudorandom functions in which the owner of the seed produces a public-key that constitutes a commi...
Zvika Brakerski, Shafi Goldwasser, Guy N. Rothblum...
STOC
2009
ACM
145views Algorithms» more  STOC 2009»
14 years 6 months ago
Non-malleable extractors and symmetric key cryptography from weak secrets
We study the question of basing symmetric key cryptography on weak secrets. In this setting, Alice and Bob share an n-bit secret W, which might not be uniformly random, but the ad...
Yevgeniy Dodis, Daniel Wichs
ICMLA
2008
13 years 6 months ago
Highly Scalable SVM Modeling with Random Granulation for Spam Sender Detection
Spam sender detection based on email subject data is a complex large-scale text mining task. The dataset consists of email subject lines and the corresponding IP address of the em...
Yuchun Tang, Yuanchen He, Sven Krasser