Person:

Vadhan, Salil

The heart of the task of building public key cryptosystems is viewed as that of "making trapdoors;" in fact, public key cryptosystems and trapdoor functions are often discussed as synonymous. How accurate is this view? In this paper we endeavor to get a better understanding of the nature of "trapdoorness" and its relation to public key cryptosystems, by broadening the scope of the investigation: we look at general trapdoor functions; that is, functions that are not necessarily injective (ie., one-to-one). Our first result is somewhat surprising: we show that non-injective trapdoor functions (with super-polynomial pre-image size) can be constructed from any one-way function (and hence it is unlikely that they suffice for public key encryption). On the other hand, we show that trapdoor functions with polynomial pre-image size are sufficient for public key encryption. Together, these two results indicate that the pre-image size is a fundamental parameter of trapdoor functions. We then turn our attention to the converse, asking what kinds of trapdoor functions can be constructed from public key cryptosystems. We take a first step by showing that in the random-oracle model one can construct injective trapdoor functions from any public key cryptosystem.

Exploring and mapping an unknown environment is a fundamental problem that is studied in a variety of contexts. Many results have focused on finding efficient solutions to restricted versions of the problem. In this paper, we consider a model that makes very limited assumptions about the environment and solve the mapping problem in this general setting. We model the environment by an unknown directed graph G, and consider the problem of a robot exploring and mapping G. The edges emanating from each vertex are numbered from ‘1’ to ‘d’, but we do not assume that the vertices of G are labeled. Since the robot has no way of distinguishing between vertices, it has no hope of succeeding unless it is given some means of distinguishing between vertices. For this reason we provide the robot with a “pebble”—a device that it can place on a vertex and use to identify the vertex later. In this paper we show: (1) If the robot knows an upper bound on the number of vertices then it can learn the graph efficiently with only one pebble. (2) If the robot does not know an upper bound on the number of vertices n, then Θ(log log n) pebbles are both necessary and sufficient. In both cases our algorithms are deterministic.

We extend the study of non-interactive statistical zero-knowledge proofs. Our main focus is to compare the class NISZK of problems possessing such non-interactive proofs to the class SZK of problems possessing interactive statistical zero-knowledge proofs. Along these lines, we first show that if statistical zero knowledge is non-trivial then so is non-interactive statistical zero knowledge, where by non-trivial we mean that the class includes problems which are not solvable in probabilistic polynomial-time. (The hypothesis holds under various assumptions, such as the intractability of the Discrete Logarithm Problem.) Furthermore, we show that if NISZK is closed under complement, then in fact SZK=NISZK, i.e. all statistical zero-knowledge proofs can be made non-interactive.

The main tools in our analysis are two promise problems that are natural restrictions of promise problems known to be complete for SZK. We show that these restricted problems are in fact complete for NISZK and use this relationship to derive our results comparing the two classes. The two problems refer to the statistical difference, and difference in entropy, respectively, of a given distribution from the uniform one. We also consider a weak form of NISZK, in which only requires that for every inverse polynomial 1/p(n), there exists a simulator which achieves simulator deviation 1/p(n), and show that this weak form of NISZK actually equals NISZK

Informally, an obfuscator O is an (efficient, probabilistic) "compiler" that takes as input a program (or circuit) P and produces a new program O(P) that has the same functionality as P yet is "unintelligible" in some sense. Obfuscators, if they exist, would have a wide variety of cryptographic and complexity-theoretic applications, ranging from software protection to homomorphic encryption to complexity-theoretic analogues of Rice's theorem. Most of these applications are based on an interpretation of the "unintelligibility" condition in obfuscation as meaning that O(P) is a "virtual black box," in the sense that anything one can efficiently compute given O(P), one could also efficiently compute given oracle access to P.

In this work, we initiate a theoretical investigation of obfuscation. Our main result is that, even under very weak formalizations of the above intuition, obfuscation is impossible. We prove this by constructing a family of programs F that are unobfuscatable in the sense that (a) given any efficient program Pi that computes the same function as a program P\in F, the “source code” P can be efficiently reconstructed, yet (b) given oracle access to a (randomly selected) program P\in F, no efficient algorithm can reconstruct P (or even distinguish a certain bit in the code from random) except with negligible probability.

We extend our impossibility result in a number of ways, including even obfuscators that (a) are not necessarily computable in polynomial time, (b) only approximately preserve the functionality, and (c) only need to work for very restricted models of computation (TC_0). We also rule out several potential applications of obfuscators, by constructing “unobfuscatable” signature schemes, encryption schemes, and pseudorandom function families.

We present the first complete problem for SZK, the class of (promise) problems possessing statistical zero-knowledge proofs (against an honest verifier). The problem, called STATISTICAL DIFFERENCE, is to decide whether two efficiently samplable distributions are either statistically close or far apart. This gives a new characterization of SZK that makes no reference to interaction or zero knowledge.

We propose the use of complete problems to unify and extend the study of statistical zero knowledge. To this end, we examine several consequences of our Completeness Theorem and its proof, such as:

* A way to make every (honest-verifier) statistical zero-knowledge proof very communication efficient, with the prover sending only one bit to the verifier (to achieve soundness error 1/2).
* Simpler proofs of many of the previously known results about statistical zero knowledge, such as the Fortnow and Aiello--Håstad upper bounds on the complexity of SZK and Okamoto's result that SZK is closed under complement.
* Strong closure properties of SZK which amount to constructing statistical zero-knowledge proofs for complex assertions built out of simpler assertions already shown to be in SZK.
* New results about the various measures of "knowledge complexity," including a collapse in the hierarchy corresponding to knowledge complexity in the "hint" sense.
* Algorithms for manipulating the statistical difference between efficiently samplable distributions, including transformations which "polarize" and "reverse" the statistical relationship between a pair of distributions.

We show how to transform any interactive proof system which is statistical zero-knowledge with respect to the honest-verifier, into a proof system which is statistical zero-knowledge with respect to any verifier. This is done by limiting the behavior of potentially cheating verifiers, without using computational assumptions or even referring to the complexity of such verifier strategies. (Previous transformations have either relied on computational assumptions or were applicable only to constant-round public-coin proof systems.)

Our transformation also applies to public-coin (aka Arthur-Merlin) computational zero-knowledge proofs: We transform any Arthur-Merlin proof system which is computational zero-knowledge with respect to the honest-verifier, into an Arthur-Merlin proof system which is computational zero-knowledge with respect to any probabilistic polynomial-time verifier.

A crucial ingredient in our analysis is a new lemma regarding 2-universal hashing functions.

Informally, an obfuscator O is an (efficient, probabilistic) “compiler” that takes as input a program (or circuit) P and produces a new program O(P) that has the same functionality as P yet is “unintelligible” in some sense. Obfuscators, if they exist, would have a wide variety of cryptographic and complexity-theoretic applications, ranging from software protection to homomorphic encryption to complexity-theoretic analogues of Rice's theorem. Most of these applications are based on an interpretation of the “unintelligibility” condition in obfuscation as meaning that O(P) is a “virtual black box,” in the sense that anything one can efficiently compute given O(P), one could also efficiently compute given oracle access to P. In this work, we initiate a theoretical investigation of obfuscation. Our main result is that, even under very weak formalizations of the above intuition, obfuscation is impossible. We prove this by constructing a family of efficient programs P that are unobfuscatable in the sense that (a) given any efficient program P' that computes the same function as a program P ∈ p, the “source code” P can be efficiently reconstructed, yet (b) given oracle access to a (randomly selected) program P ∈ p, no efficient algorithm can reconstruct P (or even distinguish a certain bit in the code from random) except with negligible probability. We extend our impossibility result in a number of ways, including even obfuscators that (a) are not necessarily computable in polynomial time, (b) only approximately preserve the functionality, and (c) only need to work for very restricted models of computation (TC0). We also rule out several potential applications of obfuscators, by constructing “unobfuscatable” signature schemes, encryption schemes, and pseudorandom function families.