International Association for Cryptologic Research

International Association
for Cryptologic Research


Pihla Karanko


Adaptive Distributional Security for Garbling Schemes with O(|x|) Online Complexity
Garbling schemes allow to garble a circuit C and an input x such that C(x) can be computed while hiding both C and x. In the context of adaptive security, an adversary specifies the input to the circuit after seeing the garbled circuit, so that one can pre-process the garbling of C and later only garble the input x in the online phase. Since the online phase may be time-critical, it is an interesting question how much information needs to be transmitted in this phase and ideally, this should be close to |x|. Unfortunately, Applebaum, Ishai, Kushilevitz, and Waters (AIKW, CRYPTO 2013) show that for some circuits, specifically PRGs, achieving online complexity close to |x| is impossible with simulation-based security, and Hubácek and Wichs (HW, ITCS 2015) show that online complexity of maliciously secure 2-party computation needs to grow with the incompressibility entropy of the function. We thus seek to understand under which circumstances optimal online complexity is feasible despite these strong lower bounds. Our starting point is the observation that lower bounds (only) concern cryptographic circuits and that, when an embedded secret is not known to the adversary (distinguisher), then the lower bound techniques do not seem to apply. Our main contribution is distributional simulation-based security (DSIM), a framework for capturing weaker, yet meaningful simulation-based (adaptive) security which does not seem to suffer from impossibility results akin to AIKW. We show that DSIM can be used to prove security of a distributed symmetric encryption protocol built around garbling. We also establish a bootstrapping result from DSIM-security for NC0 circuits to DSIM-security for arbitrary polynomial-size circuits while preserving their online complexity.
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.