From Non-Adaptive to Adaptive Pseudorandom Functions

Unlike the standard notion of pseudorandom functions (PRF), a non-adaptive PRF is only required to be indistinguishable from a random function in the eyes of a non-adaptive distinguisher (i.e., one that prepares its oracle calls in advance). A recent line of research has studied the possibility of a direct construction of adaptive PRFs from non-adaptive ones, where direct means that the constructed adaptive PRF uses only few (ideally, constant number of) calls to the underlying non-adaptive PRF. Unfortunately, this study has only yielded negative results, showing that ``natural such constructions are unlikely to exist (e.g., Myers [EUROCRYPT \u2704], Pietrzak05 [CRYPTO \u2705, EUROCRYPT \u2706]). We give an affirmative answer to the above question, presenting a direct construction of adaptive PRFs from non-adaptive ones. The suggested construction is extremely simple, a composition of the non-adaptive PRF with an appropriate pairwise independent hash function

Pseudorandomness for Approximate Counting and Sampling

We study computational procedures that use both randomness and nondeterminism. The goal of this paper is to derandomize such procedures under the weakest possible assumptions. Our main technical contribution allows one to “boost” a given hardness assumption: We show that if there is a problem in EXP that cannot be computed by poly-size nondeterministic circuits then there is one which cannot be computed by poly-size circuits that make non-adaptive NP oracle queries. This in particular shows that the various assumptions used over the last few years by several authors to derandomize Arthur-Merlin games (i.e., show AM = NP) are in fact all equivalent. We also define two new primitives that we regard as the natural pseudorandom objects associated with approximate counting and sampling of NP-witnesses. We use the “boosting” theorem and hashing techniques to construct these primitives using an assumption that is no stronger than that used to derandomize AM. We observe that Cai's proof that S_2^P ⊆ PP⊆(NP) and the learning algorithm of Bshouty et al. can be seen as reductions to sampling that are not probabilistic. As a consequence they can be derandomized under an assumption which is weaker than the assumption that was previously known to suffice

Bloom Filters in Adversarial Environments

Many efficient data structures use randomness, allowing them to improve upon deterministic ones. Usually, their efficiency and correctness are analyzed using probabilistic tools under the assumption that the inputs and queries are independent of the internal randomness of the data structure. In this work, we consider data structures in a more robust model, which we call the adversarial model. Roughly speaking, this model allows an adversary to choose inputs and queries adaptively according to previous responses. Specifically, we consider a data structure known as "Bloom filter" and prove a tight connection between Bloom filters in this model and cryptography. A Bloom filter represents a set $S$ of elements approximately, by using fewer bits than a precise representation. The price for succinctness is allowing some errors: for any $x \in S$ it should always answer `Yes', and for any $x \notin S$ it should answer `Yes' only with small probability. In the adversarial model, we consider both efficient adversaries (that run in polynomial time) and computationally unbounded adversaries that are only bounded in the number of queries they can make. For computationally bounded adversaries, we show that non-trivial (memory-wise) Bloom filters exist if and only if one-way functions exist. For unbounded adversaries we show that there exists a Bloom filter for sets of size $n$ and error $\varepsilon$, that is secure against $t$ queries and uses only $O(n \log{\frac{1}{\varepsilon}}+t)$ bits of memory. In comparison, $n\log{\frac{1}{\varepsilon}}$ is the best possible under a non-adaptive adversary

A Hypergraph Dictatorship Test with Perfect Completeness

A hypergraph dictatorship test is first introduced by Samorodnitsky and Trevisan and serves as a key component in their unique games based \PCP construction. Such a test has oracle access to a collection of functions and determines whether all the functions are the same dictatorship, or all their low degree influences are $o(1).$ Their test makes $q\geq3$ queries and has amortized query complexity $1+O(\frac{\log q}{q})$ but has an inherent loss of perfect completeness. In this paper we give an adaptive hypergraph dictatorship test that achieves both perfect completeness and amortized query complexity $1+O(\frac{\log q}{q})$.Comment: Some minor correction

The Limits of Post-Selection Generalization

While statistics and machine learning offers numerous methods for ensuring generalization, these methods often fail in the presence of adaptivity---the common practice in which the choice of analysis depends on previous interactions with the same dataset. A recent line of work has introduced powerful, general purpose algorithms that ensure post hoc generalization (also called robust or post-selection generalization), which says that, given the output of the algorithm, it is hard to find any statistic for which the data differs significantly from the population it came from. In this work we show several limitations on the power of algorithms satisfying post hoc generalization. First, we show a tight lower bound on the error of any algorithm that satisfies post hoc generalization and answers adaptively chosen statistical queries, showing a strong barrier to progress in post selection data analysis. Second, we show that post hoc generalization is not closed under composition, despite many examples of such algorithms exhibiting strong composition properties

Efficiency Improvements in Constructing Pseudorandom Generators from One-way Functions

ABSTRACT We give a new construction of pseudorandom generators from any one-way function. The construction achieves better parameters and is simpler than that given in the seminal work of Håstad, Impagliazzo, Levin, and Luby [SICOMP '99]. The key to our construction is a new notion of nextblock pseudoentropy, which is inspired by the notion of "inaccessible entropy" recently introduced in [Haitner, Reingold, Vadhan, and Wee, STOC '09]. An additional advantage over previous constructions is that our pseudorandom generators are parallelizable and invoke the one-way function in a non-adaptive manner. Using [Applebaum, Ishai, and Kushilevitz, SICOMP '06], this implies the existence of pseudorandom generators in NC 0 based on the existence of one-way functions in NC 1

On the complexity of constructing pseudorandom functions (especially when they don\u27t exist)

We study the complexity of black-box constructions of pseudorandom functions (PRF) from one-way functions (OWF) that are secure against non-uniform adversaries. We show that if OWF do not exist, then given as an oracle any (inefficient) hard-to-invert function, one can compute a PRF in polynomial time with only k(n) oracle queries, for any k(n) = \omega(1) (e.g. k(n) = log^* n). Combining this with the fact that OWF imply PRF, we show that unconditionally there exists a (pathological) construction of PRF from OWF making at most k(n) queries. This result shows a limitation of a certain class of techniques for proving efficiency lower bounds on the construction of PRF from OWF. Our result builds on the work of Reingold, Trevisan, and Vadhan (TCC ’04), who show that when OWF do not exist there is a pseudorandom generator (PRG) construction that makes only one oracle query to the hard-to-invert function. Our proof combines theirs with the Nisan-Wigderson generator (JCSS ’94), and with a recent technique by Berman and Haitner (TCC ’12). Working in the same context (i.e. when OWF do not exist), we also construct a poly-time PRG with arbitrary polynomial stretch that makes non-adaptive queries to an (inefficient) one-bit-stretch oracle PRG. This contrasts with the well-known adaptive stretch-increasing construction due to Goldreich and Micali. Both above constructions simply apply an affine function (parity or its complement) to the query answers. We complement this by showing that if the post-processing is restricted to only taking projections then non-adaptive constructions of PRF, or even linear-stretch PRG, can be ruled out