Topic
String (computer science)
About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.
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TL;DR: A general framework suitable to capture the essence of compressed pattern matching according to various dictionary-based compressions is introduced, which includes such compression methods as Lempel-Ziv family, RE-PAIR, SEQUITUR, and the static Dictionary-based method.
109 citations
06 May 2001
TL;DR: A probabilistic construction of all-or-nothing transforms and exposure-resilient functions is given which is essentially optimal and substantially improves on previous constructions of [19, 5], as a result of which nearly optimal adaptively secure ERF's and AONT's are obtained.
Abstract: We consider the question of adaptive security for two related cryptographic primitives: all-or-nothing transforms and exposure-resilient functions. Both are concerned with retaining security when an intruder learns some bits of a string which is supposed to be secret: all-or-nothing transforms (AONT) protect their input even given partial knowledge of the output; exposure-resilient functions (ERF) hide their output even given partial exposure of their input. Both of these primitives can be defined in the perfect, statistical and computational settings and have a variety of applications in cryptography. In this paper, we study how these notions fare against adaptive adversaries, who may choose which positions of a secret string to observe on the fly.
In the perfect setting, we prove a new, strong lower bound on the constructibility of (perfect) AONT. This applies to both standard and adaptively secure AONT. In particular, to hide an input as short as log n bits, the adversary must see no more than half of the n-bit output. This bound also provides a new impossibility result on the existence of (ramp) secret-sharing schemes [6] and relates to a combinatorial problem of independent interest: finding "balanced" colorings of the hypercube.
In the statistical setting, we show that adaptivity adds strictly more power to the adversary. We relate and reduce the construction of adaptive ERF's to that of almost-perfect resilient functions [19], for which the adversary can actually set some of the input positions and still learn nothing about the output. We give a probabilistic construction of these functions which is essentially optimal and substantially improves on previous constructions of [19, 5]. As a result, we get nearly optimal adaptively secure ERF's and AONT's. Finally, extending the statistical construction we obtain optimal computational adaptive ERF's, "public-value" AONT's and resilient functions.
109 citations
TL;DR: String matching has sparked renewed research interest due to its usefulness for deep packet inspection in applications such as intrusion detection, virus scanning, and Internet content filtering.
Abstract: String matching has sparked renewed research interest due to its usefulness for deep packet inspection in applications such as intrusion detection, virus scanning, and Internet content filtering. Matching expressive pattern specifications with a scalable and efficient design, accelerating the entire packet flow, and string matching with high-level semantics are promising topics for further study.
109 citations
TL;DR: While there is a strong focus on neural network applications in unsupervised, supervised and reinforcement learning, other machine learning techniques are discussed as well, including various clustering and anomaly detection algorithms, support vector machines, and decision trees.
109 citations