1,541 research outputs found
Constructions of Almost Optimal Resilient Boolean Functions on Large Even Number of Variables
In this paper, a technique on constructing nonlinear resilient Boolean
functions is described. By using several sets of disjoint spectra functions on
a small number of variables, an almost optimal resilient function on a large
even number of variables can be constructed. It is shown that given any ,
one can construct infinitely many -variable ( even), -resilient
functions with nonlinearity . A large class of highly
nonlinear resilient functions which were not known are obtained. Then one
method to optimize the degree of the constructed functions is proposed. Last,
an improved version of the main construction is given.Comment: 14 pages, 2 table
Approximate resilience, monotonicity, and the complexity of agnostic learning
A function is -resilient if all its Fourier coefficients of degree at
most are zero, i.e., is uncorrelated with all low-degree parities. We
study the notion of of Boolean
functions, where we say that is -approximately -resilient if
is -close to a -valued -resilient function in
distance. We show that approximate resilience essentially characterizes the
complexity of agnostic learning of a concept class over the uniform
distribution. Roughly speaking, if all functions in a class are far from
being -resilient then can be learned agnostically in time and
conversely, if contains a function close to being -resilient then
agnostic learning of in the statistical query (SQ) framework of Kearns has
complexity of at least . This characterization is based on the
duality between approximation by degree- polynomials and
approximate -resilience that we establish. In particular, it implies that
approximation by low-degree polynomials, known to be sufficient for
agnostic learning over product distributions, is in fact necessary.
Focusing on monotone Boolean functions, we exhibit the existence of
near-optimal -approximately
-resilient monotone functions for all
. Prior to our work, it was conceivable even that every monotone
function is -far from any -resilient function. Furthermore, we
construct simple, explicit monotone functions based on and that are close to highly resilient functions. Our constructions are
based on a fairly general resilience analysis and amplification. These
structural results, together with the characterization, imply nearly optimal
lower bounds for agnostic learning of monotone juntas
Two-Source Condensers with Low Error and Small Entropy Gap via Entropy-Resilient Functions
In their seminal work, Chattopadhyay and Zuckerman (STOC\u2716) constructed a two-source extractor with error epsilon for n-bit sources having min-entropy {polylog}(n/epsilon). Unfortunately, the construction\u27s running-time is {poly}(n/epsilon), which means that with polynomial-time constructions, only polynomially-small errors are possible. Our main result is a {poly}(n,log(1/epsilon))-time computable two-source condenser. For any k >= {polylog}(n/epsilon), our condenser transforms two independent (n,k)-sources to a distribution over m = k-O(log(1/epsilon)) bits that is epsilon-close to having min-entropy m - o(log(1/epsilon)). Hence, achieving entropy gap of o(log(1/epsilon)).
The bottleneck for obtaining low error in recent constructions of two-source extractors lies in the use of resilient functions. Informally, this is a function that receives input bits from r players with the property that the function\u27s output has small bias even if a bounded number of corrupted players feed adversarial inputs after seeing the inputs of the other players. The drawback of using resilient functions is that the error cannot be smaller than ln r/r. This, in return, forces the running time of the construction to be polynomial in 1/epsilon.
A key component in our construction is a variant of resilient functions which we call entropy-resilient functions. This variant can be seen as playing the above game for several rounds, each round outputting one bit. The goal of the corrupted players is to reduce, with as high probability as they can, the min-entropy accumulated throughout the rounds. We show that while the bias decreases only polynomially with the number of players in a one-round game, their success probability decreases exponentially in the entropy gap they are attempting to incur in a repeated game
Randomness Extraction in AC0 and with Small Locality
Randomness extractors, which extract high quality (almost-uniform) random
bits from biased random sources, are important objects both in theory and in
practice. While there have been significant progress in obtaining near optimal
constructions of randomness extractors in various settings, the computational
complexity of randomness extractors is still much less studied. In particular,
it is not clear whether randomness extractors with good parameters can be
computed in several interesting complexity classes that are much weaker than P.
In this paper we study randomness extractors in the following two models of
computation: (1) constant-depth circuits (AC0), and (2) the local computation
model. Previous work in these models, such as [Vio05a], [GVW15] and [BG13],
only achieve constructions with weak parameters. In this work we give explicit
constructions of randomness extractors with much better parameters. As an
application, we use our AC0 extractors to study pseudorandom generators in AC0,
and show that we can construct both cryptographic pseudorandom generators
(under reasonable computational assumptions) and unconditional pseudorandom
generators for space bounded computation with very good parameters.
Our constructions combine several previous techniques in randomness
extractors, as well as introduce new techniques to reduce or preserve the
complexity of extractors, which may be of independent interest. These include
(1) a general way to reduce the error of strong seeded extractors while
preserving the AC0 property and small locality, and (2) a seeded randomness
condenser with small locality.Comment: 62 page
Implementing Symmetric Cryptography Using Sequence of Semi-Bent Functions
Symmetric cryptography is a cornerstone of everyday digital security, where two parties must share a common key to communicate. The most common primitives in symmetric cryptography are stream ciphers and block ciphers that guarantee confidentiality of communications and hash functions for integrity. Thus, for securing our everyday life communication, it is necessary to be convinced by the security level provided by all the symmetric-key cryptographic primitives. The most important part of a stream cipher is the key stream generator, which provides the overall security for stream ciphers. Nonlinear Boolean functions were preferred for a long time to construct the key stream generator. In order to resist several known attacks, many requirements have been proposed on the Boolean functions. Attacks against the cryptosystems have forced deep research on Boolean function to allow us a more secure encryption. In this work we describe all main requirements for constructing of cryptographically significant Boolean functions. Moreover, we provide a construction of Boolean functions (semi-bent Boolean functions) which can be used in the construction of orthogonal variable spreading factor codes used in code division multiple access (CDMA) systems as well as in certain cryptographic applications
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