Searching for ELFs in the Cryptographic Forest
Extremely Lossy Functions (ELFs) are families of functions that, depending on the choice during key generation, either operate in injective mode or instead have only a polynomial image size. The choice of the mode is indistinguishable to an outsider. ELFs were introduced by Zhandry (Crypto 2016) and have been shown to be very useful in replacing random oracles in a number of applications. One open question is to determine the minimal assumption needed to instantiate ELFs. While all constructions of ELFs depend on some form of exponentially-secure public-key primitive, it was conjectured that exponentially-secure secret-key primitives, such as one-way functions, hash functions or one-way product functions, might be sufficient to build ELFs. In this work we answer this conjecture mostly negative: We show that no primitive, which can be derived from a random oracle (which includes all secret-key primitives mentioned above), is enough to construct even moderately lossy functions in a black-box manner. However, we also show that (extremely) lossy functions themselves do not imply public-key cryptography, leaving open the option to build ELFs from some intermediate primitive between the classical categories of secret-key and public-key cryptography.
Single-to-Multi-Theorem Transformations for Non-Interactive Statistical Zero-Knowledge 📺
Non-interactive zero-knowledge proofs or arguments allow a prover to show validity of a statement without further interaction. For non-trivial statements such protocols require a setup assumption in form of a common random or reference string (CRS). Generally, the CRS can only be used for one statement (single-theorem zero-knowledge) such that a fresh CRS would need to be generated for each proof. Fortunately, Feige, Lapidot and Shamir (FOCS 1990) presented a transformation for any non-interactive zero-knowledge proof system that allows the CRS to be reused any polynomial number of times (multi-theorem zero-knowledge). This FLS transformation, however, is only known to work for either computational zero-knowledge or requires a structured, non-uniform common reference string. In this paper we present FLS-like transformations that work for non-interactive statistical zero-knowledge arguments in the common random string model. They allow to go from single-theorem to multi-theorem zero-knowledge and also preserve soundness, for both properties in the adaptive and non-adaptive case. Our first transformation is based on the general assumption that one-way permutations exist, while our second transformation uses lattice-based assumptions. Additionally, we define different possible soundness notions for non-interactive arguments and discuss their relationships.
On Derandomizing Yao’s Weak-to-Strong OWF Construction 📺
The celebrated result of Yao (Yao, FOCS'82) shows that concatenating n · p(n) copies of a weak one-way function f which can be inverted with probability 1 - 1/p(n) suffices to construct a strong one-way function g, showing that weak and strong one-way functions are black-box equivalent. This direct product theorem for hardness amplification of one-way functions has been very influential. However, the construction of Yao has severe efficiency limitations; in particular, it is not security-preserving (the input to g needs to be much larger than the input to f). Understanding whether this is inherent is an intriguing and long-standing open question. In this work, we explore necessary features of constructions which achieve short input length by proving the following: for any direct product construction of strong OWF g from a weak OWF f, which can be inverted with probability 1-1/p(n), the input size of g must grow as Omega(p(n)). By direct product construction, we refer to any construction with the following structure: the construction g executes some arbitrary pre-processing function (independent of f) on its input, obtaining a vector (y_1 ,··· ,y_l ), and outputs f(y_1),··· ,f(y_l). Note that Yao's construction is obtained by setting the pre-processing to be the identity. Our result generalizes to functions g with post-processing, as long as the post-processing function is not too lossy. Thus, in essence, any weak-to-strong hardness amplification must either (1) be very far from security-preserving, (2) use adaptivity, or (3) must be very far from a direct-product structure (in the sense of having a very lossy post-processing of the outputs of f). On a technical level, we use ideas from lower bounds for secret-sharing to prove the impossibility of derandomizing Yao in a black-box way. Our results are in line with Goldreich, Impagliazzo, Levin, Venkatesan, and Zuckerman (FOCS 1990) who derandomize Yao's construction for regular weak one-way functions by evaluating the OWF along a random walk on an expander graph---the construction is adaptive, since it alternates steps on the expander graph with evaluations of the weak one-way function.