A set F of Boolean functions is called a pseudorandom function gen-erator (PRFG) if
communicating with a randomly chosen secret function from F cannot be efficiently …

This paper investigates which complexity classes inside NC can contain pseudorandom
function generators (PRFGs). Under the Decisional Diffie-Hellman assumption (a common …

We investigate cryptographic lower bounds on the number of samples and on computational
resources required to learn several classes of boolean circuits on the uniform distribution …

Over the years a range of positive algorithmic results have been obtained for learning
various classes of monotone Boolean functions from uniformly distributed random examples …

A wide range of positive and negative results have been established for learning different
classes of Boolean functions from uniformly distributed random examples. However …

This article extends and improves the work of Fortnow and Klivans [2009], who showed that
if a circuit class C has an efficient learning algorithm in Angluin's model of exact learning via …

In this paper, we prove the intractability of learning several classes of Boolean functions in
the distribution-free model (also called the Probably Approximately Correct or PAC model) of …

Constrained pseudorandom functions (introduced independently by Boneh and Waters
(CCS 2013), Boyle, Goldwasser, and Ivan (PKC 2014), and Kiayias, Papadopoulos …

We efficiently combine unpredictability and verifiability by extending the Goldreich-
Goldwasser-Micali (1986) construction of pseudorandom functions f/sub s/from a secret …

We investigate cryptographic lower bounds on the learnability of Boolean formulas and
constant depth circuits on the {niform distribution and other specifi; distributions. We first …