International Association for Cryptologic Research

International Association
for Cryptologic Research


Sina Shiehian


Constraining and Watermarking PRFs from Milder Assumptions 📺
Chris Peikert Sina Shiehian
Constrained pseudorandom functions (C-PRFs) let the possessor of a secret key delegate the ability to evaluate the function on certain authorized inputs, while keeping the remaining function values pseudorandom. A constraint-hiding constrained PRF (CHC-PRF) additionally conceals the predicate that determines which inputs are authorized. These primitives have a wealth of applications, including watermarking schemes, symmetric deniable encryption, and updatable garbled circuits. Recent works have constructed (CH)C-PRFs from rather aggressive parameterizations of Learning With Errors (LWE) with subexponential modulus-noise ratios, even for relatively simple “puncturing” or $$ ext {NC}^{1}$$ circuit constraints. This corresponds to strong lattice assumptions and inefficient constructions, and stands in contrast to LWE-based unconstrained PRFs and fully homomorphic encryption schemes, which can be based on quasi-polynomial or even (nearly) polynomial modulus-noise ratios. In this work we considerably improve the LWE assumptions needed for building (constraint-hiding) constrained PRFs and watermarking schemes. In particular, for CHC-PRFs and related watermarking schemes we improve the modulus-noise ratio to $$lambda ^{O((d+log lambda ) log lambda )}$$ for depth- d circuit constraints, which is merely quasi-polynomial for $$ ext {NC}^{1}$$ circuits and closely related watermarking schemes. For (constraint-revealing) C-PRFs for $$ ext {NC}^{1}$$ we do even better, obtaining a nearly polynomial $$lambda ^{omega (1)}$$ ratio. These improvements are partly enabled by slightly modifying the definition of C-PRFs, in a way that is still compatible with many of their applications. Finally, as a contribution of independent interest we build CHC-PRFs for special constraint classes from generic , weaker assumptions: we obtain bit-fixing constraints based on the minimal assumption of one-way functions, and hyperplane-membership constraints based on key-homomorphic PRFs.
Noninteractive Zero Knowledge for NP from (Plain) Learning with Errors 📺
Chris Peikert Sina Shiehian
We finally close the long-standing problem of constructing a noninteractive zero-knowledge (NIZK) proof system for any NP language with security based on the plain Learning With Errors (LWE) problem, and thereby on worst-case lattice problems. Our proof system instantiates the framework recently developed by Canetti et al.  [EUROCRYPT’18], Holmgren and Lombardi [FOCS’18], and Canetti et al.  [STOC’19] for soundly applying the Fiat–Shamir transform using a hash function family that is correlation intractable for a suitable class of relations. Previously, such hash families were based either on “exotic” assumptions (e.g., indistinguishability obfuscation or optimal hardness of certain LWE variants) or, more recently, on the existence of circularly secure fully homomorphic encryption (FHE). However, none of these assumptions are known to be implied by plain LWE or worst-case hardness.Our main technical contribution is a hash family that is correlation intractable for arbitrary size-S circuits, for any polynomially bounded S, based on plain LWE (with small polynomial approximation factors). The construction combines two novel ingredients: a correlation-intractable hash family for log-depth circuits based on LWE (or even the potentially harder Short Integer Solution problem), and a “bootstrapping” transform that uses (leveled) FHE to promote correlation intractability for the FHE decryption circuit to arbitrary (bounded) circuits. Our construction can be instantiated in two possible “modes,” yielding a NIZK that is either computationally sound and statistically zero knowledge in the common random string model, or vice-versa in the common reference string model.
Privately Constraining and Programming PRFs, the LWE Way
Chris Peikert Sina Shiehian
Constrained pseudorandom functions allow for delegating “constrained” secret keys that let one compute the function at certain authorized inputs—as specified by a constraining predicate—while keeping the function value at unauthorized inputs pseudorandom. In the constraint-hiding variant, the constrained key hides the predicate. On top of this, programmable variants allow the delegator to explicitly set the output values yielded by the delegated key for a particular set of unauthorized inputs.Recent years have seen rapid progress on applications and constructions of these objects for progressively richer constraint classes, resulting most recently in constraint-hiding constrained PRFs for arbitrary polynomial-time constraints from Learning With Errors (LWE) [Brakerski, Tsabary, Vaikuntanathan, and Wee, TCC’17], and privately programmable PRFs from indistinguishability obfuscation (iO) [Boneh, Lewi, and Wu, PKC’17].In this work we give a unified approach for constructing both of the above kinds of PRFs from LWE with subexponential $$\exp (n^{\varepsilon })$$exp(nε) approximation factors. Our constructions follow straightforwardly from a new notion we call a shift-hiding shiftable function, which allows for deriving a key for the sum of the original function and any desired hidden shift function. In particular, we obtain the first privately programmable PRFs from non-iO assumptions.


Chris Peikert (4)