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118
How to Go Beyond the BlackBox Simulation Barrier
 In 42nd FOCS
, 2001
"... The simulation paradigm is central to cryptography. A simulator is an algorithm that tries to simulate the interaction of the adversary with an honest party, without knowing the private input of this honest party. Almost all known simulators use the adversary’s algorithm as a blackbox. We present t ..."
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Cited by 214 (13 self)
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The simulation paradigm is central to cryptography. A simulator is an algorithm that tries to simulate the interaction of the adversary with an honest party, without knowing the private input of this honest party. Almost all known simulators use the adversary’s algorithm as a blackbox. We present the first constructions of nonblackbox simulators. Using these new nonblackbox techniques we obtain several results that were previously proven to be impossible to obtain using blackbox simulators. Specifically, assuming the existence of collision resistent hash functions, we construct a new zeroknowledge argument system for NP that satisfies the following properties: 1. This system has a constant number of rounds with negligible soundness error. 2. It remains zero knowledge even when composed concurrently n times, where n is the security parameter. Simultaneously obtaining 1 and 2 has been recently proven to be impossible to achieve using blackbox simulators. 3. It is an ArthurMerlin (public coins) protocol. Simultaneously obtaining 1 and 3 was known to be impossible to achieve with a blackbox simulator. 4. It has a simulator that runs in strict polynomial time, rather than in expected polynomial time. All previously known constantround, negligibleerror zeroknowledge arguments utilized expected polynomialtime simulators.
SIA: Secure Information Aggregation in Sensor Networks
, 2003
"... Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of sensor networks faces many challenges imposed by realworld demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications se ..."
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Cited by 175 (11 self)
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Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of sensor networks faces many challenges imposed by realworld demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the sensor's cryptographic keys. One of the basic and indispensable functionalities of sensor networks is the ability to answer queries over the data acquired by the sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large sensor networks particularly challenging.
How to Construct ConstantRound ZeroKnowledge Proof Systems for NP
 Journal of Cryptology
, 1995
"... Constantround zeroknowledge proof systems for every language in NP are presented, assuming the existence of a collection of clawfree functions. In particular, it follows that such proof systems exist assuming the intractability of either the Discrete Logarithm Problem or the Factoring Problem for ..."
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Cited by 157 (8 self)
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Constantround zeroknowledge proof systems for every language in NP are presented, assuming the existence of a collection of clawfree functions. In particular, it follows that such proof systems exist assuming the intractability of either the Discrete Logarithm Problem or the Factoring Problem for Blum Integers.
A Sanctuary for Mobile Agents
, 1997
"... The Sanctuary project at UCSD is building a secure infrastructure for mobile agents, and examining ..."
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Cited by 124 (4 self)
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The Sanctuary project at UCSD is building a secure infrastructure for mobile agents, and examining
NonInteractive Verifiable Computing: Outsourcing Computation to Untrusted Workers
, 2009
"... Verifiable Computation enables a computationally weak client to “outsource ” the computation of a function F on various inputs x1,...,xk to one or more workers. The workers return the result of the function evaluation, e.g., yi = F(xi), as well as a proof that the computation of F was carried out co ..."
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Cited by 97 (10 self)
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Verifiable Computation enables a computationally weak client to “outsource ” the computation of a function F on various inputs x1,...,xk to one or more workers. The workers return the result of the function evaluation, e.g., yi = F(xi), as well as a proof that the computation of F was carried out correctly on the given value xi. The verification of the proof should require substantially less computational effort than computing F(xi) from scratch. We present a protocol that allows the worker to return a computationallysound, noninteractive proof that can be verified in O(m) time, where m is the bitlength of the output of F. The protocol requires a onetime preprocessing stage by the client which takes O(C) time, where C is the smallest Boolean circuit computing F. Our scheme also provides input and output privacy for the client, meaning that the workers do not learn any information about the xi or yi values. 1
COMPUTATIONALLY SOUND PROOFS
, 2000
"... This paper puts forward a new notion of a proof based on computational complexity and explores its implications for computation at large. Computationally sound proofs provide, in a novel and meaningful framework, answers to old and new questions in complexity theory. In particular, given a random o ..."
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Cited by 92 (3 self)
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This paper puts forward a new notion of a proof based on computational complexity and explores its implications for computation at large. Computationally sound proofs provide, in a novel and meaningful framework, answers to old and new questions in complexity theory. In particular, given a random oracle or a new complexity assumption, they enable us to 1. prove that verifying is easier than deciding for all theorems; 2. provide a quite effective way to prove membership in computationally hard languages (such as CoNPcomplete ones); and 3. show that every computation possesses a short certificate vouching its correctness. Finally, if a special type of computationally sound proof exists, we show that Blum’s notion of program checking can be meaningfully broadened so as to prove that NPcomplete languages are checkable.
Robust PCPs of Proximity, Shorter PCPs and Applications to Coding
 in Proc. 36th ACM Symp. on Theory of Computing
, 2004
"... We continue the study of the tradeo between the length of PCPs and their query complexity, establishing the following main results (which refer to proofs of satis ability of circuits of size n): 1. We present PCPs of length exp( ~ O(log log n) ) n that can be veri ed by making o(log log n) ..."
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Cited by 79 (26 self)
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We continue the study of the tradeo between the length of PCPs and their query complexity, establishing the following main results (which refer to proofs of satis ability of circuits of size n): 1. We present PCPs of length exp( ~ O(log log n) ) n that can be veri ed by making o(log log n) Boolean queries.
Delegating computation: interactive proofs for muggles
 In Proceedings of the ACM Symposium on the Theory of Computing (STOC
, 2008
"... In this work we study interactive proofs for tractable languages. The (honest) prover should be efficient and run in polynomial time, or in other words a “muggle”. 1 The verifier should be superefficient and run in nearlylinear time. These proof systems can be used for delegating computation: a se ..."
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Cited by 58 (4 self)
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In this work we study interactive proofs for tractable languages. The (honest) prover should be efficient and run in polynomial time, or in other words a “muggle”. 1 The verifier should be superefficient and run in nearlylinear time. These proof systems can be used for delegating computation: a server can run a computation for a client and interactively prove the correctness of the result. The client can verify the result’s correctness in nearlylinear time (instead of running the entire computation itself). Previously, related questions were considered in the Holographic Proof setting by Babai, Fortnow, Levin and Szegedy, in the argument setting under computational assumptions by Kilian, and in the random oracle model by Micali. Our focus, however, is on the original interactive proof model where no assumptions are made on the computational power or adaptiveness of dishonest provers. Our main technical theorem gives a public coin interactive proof for any language computable by a logspace uniform boolean circuit with depth d and input length n. The verifier runs in time (n+d)·polylog(n) and space O(log(n)), the communication complexity is d · polylog(n), and the prover runs in time poly(n). In particular, for languages computable by logspace uniform N C (circuits of polylog(n) depth), the prover is efficient, the verifier runs in time n · polylog(n) and space O(log(n)), and the communication complexity is polylog(n).