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1,237
Unconditional Lower Bounds Against Advice
"... We show several unconditional lower bounds for exponential time classes against polynomial time classes with advice, including: 1. For any constant c, NEXP ̸ ⊆ P NP[nc] ..."
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Cited by 2 (0 self)
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We show several unconditional lower bounds for exponential time classes against polynomial time classes with advice, including: 1. For any constant c, NEXP ̸ ⊆ P NP[nc]
How to Use Expert Advice
 JOURNAL OF THE ASSOCIATION FOR COMPUTING MACHINERY
, 1997
"... We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worstcase situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance of the ..."
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Cited by 377 (79 self)
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is on the order of the square root of the number of mistakes of the best expert, and we give efficient algorithms that achieve this. Our upper and lower bounds have matching leading constants in most cases. We then show howthis leads to certain kinds of pattern recognition/learning algorithms with performance
A Concrete Security Treatment of Symmetric Encryption
 Proceedings of the 38th Symposium on Foundations of Computer Science, IEEE
, 1997
"... We study notions and schemes for symmetric (ie. private key) encryption in a concrete security framework. We give four di erent notions of security against chosen plaintext attack and analyze the concrete complexity ofreductions among them, providing both upper and lower bounds, and obtaining tight ..."
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Cited by 421 (65 self)
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We study notions and schemes for symmetric (ie. private key) encryption in a concrete security framework. We give four di erent notions of security against chosen plaintext attack and analyze the concrete complexity ofreductions among them, providing both upper and lower bounds, and obtaining tight
Hardness vs. randomness
 JOURNAL OF COMPUTER AND SYSTEM SCIENCES
, 1994
"... We present a simple new construction of a pseudorandom bit generator, based on the constant depth generators of [N]. It stretches a short string of truly random bits into a long string that looks random to any algorithm from a complexity class C (eg P, NC, PSPACE,...) using an arbitrary function tha ..."
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Cited by 298 (27 self)
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that is hard for C. This construction reveals an equivalence between the problem of proving lower bounds and the problem of generating good pseudorandom sequences. Our construction has many consequences. The most direct one is that efficient deterministic simulation of randomized algorithms is possible under
Randomized rumor spreading
 In IEEE Symposium on Foundations of Computer Science
, 2000
"... We investigate the class of socalled epidemic algorithms that are commonly used for the lazy transmission of updates to distributed copies of a database. These algorithms use a simple randomized communication mechanism to ensure robustness. Suppose players communicate in parallel rounds in each of ..."
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Cited by 287 (2 self)
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. Furthermore, we give a general lower bound showing that time and communicationoptimality cannot be achieved simultaneously using random phone calls, that is, every algorithm that distributes a rumor
Routing and Wavelength Assignment in AllOptical Networks
 IEEE/ACM Transactions on Networking
, 1995
"... This paper considers the problem of routing connections in a reconfigurable optical network using wavelength division multiplexing, where each connection between a pair of nodes in the network is assigned a path through the network and a wavelength on that path, such that connections whose paths sha ..."
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Cited by 264 (9 self)
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share a common link in the network are assigned different wavelengths. We derive an upper bound on the carried traffic of connections (or equivalently, a lower bound on the blocking probability) for any routing and wavelength assignment (RWA) algorithm in such a network. The bound scales
PerfectlySecure Key Distribution for Dynamic Conferences
, 1995
"... A key distribution scheme for dynamic conferences is a method by which initially an (offline) trusted server distributes private individual pieces of information to a set of users. Later, each member of any group of users of a given size (a dynamic conference) can compute a common secure group key. ..."
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Cited by 265 (5 self)
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coalitions of up to k users, that is, even if k users pool together their pieces they cannot compute anything about a key of any tsize conference comprised of other users. First we consider a noninteractive model where users compute the common key without any interaction. We prove a lower bound on the size
Unconditional Security Against MemoryBounded Adversaries
 In Advances in Cryptology – CRYPTO ’97, Lecture Notes in Computer Science
, 1997
"... We propose a privatekey cryptosystem and a protocol for key agreement by public discussion that are unconditionally secure based on the sole assumption that an adversary's memory capacity is limited. No assumption about her computing power is made. The scenario assumes that a random bit string ..."
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Cited by 49 (4 self)
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We propose a privatekey cryptosystem and a protocol for key agreement by public discussion that are unconditionally secure based on the sole assumption that an adversary's memory capacity is limited. No assumption about her computing power is made. The scenario assumes that a random bit
Entropy Measures and Unconditional Security in Cryptography
, 1997
"... One of the most important properties of a cryptographic system is a proof of its security. In the present work, informationtheoretic methods are used for proving the security of unconditionally secure cryptosystems. The security of such systems does not depend on unproven intractability assumptions ..."
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Cited by 89 (4 self)
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on privacy amplification in cryptography and on entropy smoothing in theoretical computer science. It enables a systematic investigation of the spoiling knowledge proof technique to obtain lower bounds on smooth entropy. The R'enyi entropy of order at least 2 of a random variable is a lower bound
Unconditional lower bounds for learning intersections of halfspaces
 Machine Learning
, 2007
"... We prove new lower bounds for learning intersections of halfspaces, one of the most important concept classes in computational learning theory. Our main result is that any statisticalquery algorithm for learning the intersection of √ n halfspaces in n dimensions must make 2 Ω( √ n) queries. This is ..."
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Cited by 20 (11 self)
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We prove new lower bounds for learning intersections of halfspaces, one of the most important concept classes in computational learning theory. Our main result is that any statisticalquery algorithm for learning the intersection of √ n halfspaces in n dimensions must make 2 Ω( √ n) queries
Results 1  10
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