BlindOR: An Efficient Lattice-Based Blind Signature Scheme from OR-Proofs

An OR-proof is a protocol that enables a user to prove the possession of a witness for one of two (or more) statements, without revealing which one. Abe and Okamoto (CRYPTO 2000) used this technique to build a partially blind signature scheme whose security is based on the hardness of the discrete logarithm problem. Inspired by their approach, we present BlindOR, an efficient blind signature scheme from OR-proofs based on lattices over modules. Using OR-proofs allows us to reduce the security of our scheme from the MLWE and MSIS problems, yielding a much more efficient solution compared to previous works

The Uber-Knowledge Assumption: A Bridge to the AGM

The generic-group model (GGM) and the algebraic-group model (AGM) have been immensely successful in proving the security of many classical and modern cryptosystems. These models, however, come coupled with standard-model uninstantiability results, raising the question whether the schemes analyzed under them can be based on firmer standard-model footing. We formulate the uber-knowledge (UK) assumption, a standard-model assumption that naturally extends the uber-assumption family to knowledge assumptions. We justify the soundness of the UK in both the bilinear GGM and bilinear AGM. Along the way we extend these models to incorporate hashing into groups, an adversarial capability that is available in many concrete groups. (In contrast to standard assumptions, hashing may affect the validity of knowledge assumptions.) These results, in turn, enable a modular approach to security in GGM and AGM. As example applications, we use the UK to prove knowledge-soundness of Groth16 and KZG polynomial commitments in the standard model, where for the former we reuse the existing AGM proof without hashing

Beyond Uber: Instantiating Generic Groups via PGGs

The generic-group model (GGM) has been very successful in making the analyses of many cryptographic assumptions and protocols tractable. It is, however, well known that the GGM is “uninstantiable,” i.e., there are protocols secure in the GGM that are insecure when using any real-world group. This motivates the study of standard-model notions formalizing that a real-world group in some sense “looks generic.” We introduce a standard-model definition called pseudo-generic group (PGG), where we require exponentiations with base an (initially) unknown group generator to result in random-looking group elements. In essence, our framework delicately lifts the influential notion of Universal Computational Extractors of Bellare, Hoang, and Keelveedhi (BHK, CRYPTO 2013) to a setting where the underlying ideal reference object is a generic group. The definition we obtain simultaneously generalizes the Uber assumption family, as group exponents no longer need to be polynomially induced. At the core of our definitional contribution is a new notion of algebraic unpredictability, which reinterprets the standard Schwartz–Zippel lemma as a restriction on sources. We prove the soundness of our definition in the GGM with auxiliary-input (AI-GGM). Our remaining results focus on applications of PGGs. We first show that PGGs are indeed a generalization of Uber. We then present a number of applications in settings where exponents are not polynomially induced. In particular we prove that simple variants of ElGamal meet several advanced security goals previously achieved only by complex and inefficient schemes. We also show that PGGs imply UCEs for split sources, which in turn are sufficient in several applications. As corollaries of our AI-GGM feasibility, we obtain the security of all these applications in the presence of preprocessing attacks. Some of our implications utilize a novel type of hash function, which we call linear-dependence destroyers (LDDs) and use to convert standard into algebraic unpredictability. We give an LDD for low-degree sources, and establish their plausibility for all sources by showing, via a compression argument, that random functions meet this definition