An ILP-based Proof System for the Crossing Number Problem

Formally, approaches based on mathematical programming are able to find provably optimal solutions. However, the demands on a verifiable formal proof are typically much higher than the guarantees we can sensibly attribute to implementations of mathematical programs. We consider this in the context of the crossing number problem, one of the most prominent problems in topological graph theory. The problem asks for the minimum number of edge crossings in any drawing of a given graph. Graph-theoretic proofs for this problem are known to be notoriously hard to obtain. At the same time, proofs even for very specific graphs are often of interest in crossing number research, as they can, e.g., form the basis for inductive proofs. We propose a system to automatically generate a formal proof based on an ILP computation. Such a proof is (relatively) easily verifiable, and does not require the understanding of any complex ILP codes. As such, we hope our proof system may serve as a showcase for the necessary steps and central design goals of how to establish formal proof systems based on mathematical programming formulations

Succinct Malleable NIZKs and an Application to Compact Shuffles

Depending on the application, malleability in cryptography can be viewed as either a flaw or — especially if sufficiently understood and restricted — a feature. In this vein, Chase, Kohlweiss, Lysyanskaya, and Meiklejohn recently defined malleable zero-knowledge proofs, and showed how to control the set of allowable transformations on proofs. As an application, they construct the first compact verifiable shuffle, in which one such controlled-malleable proof suffices to prove the correctness of an entire multi-step shuffle. Despite these initial steps, a number of natural open problems remain: (1) their construction of controlled-malleable proofs relies on the inherent malleability of Groth-Sahai proofs and is thus not based on generic primitives; (2) the classes of allowable transformations they can support are somewhat restrictive; and (3) their construction of a compactly verifiable shuffle has proof size O(N 2 + L) (where N is the number of votes and L is the number of mix authorities), whereas in theory such a proof could be of size O(N + L). In this paper, we address these open problems by providing a generic construction of controlledmalleable proofs using succinct non-interactive arguments of knowledge, or SNARGs for short. Our construction has the advantage that we can support a very general class of transformations (as we no longer rely on the transformations that Groth-Sahai proofs can support), and that we can use it to obtain a proof of size O(N + L) for the compactly verifiable shuffle

Efficient Bayesian Learning in Social Networks with Gaussian Estimators

We consider a group of Bayesian agents who try to estimate a state of the world $\theta$ through interaction on a social network. Each agent $v$ initially receives a private measurement of $\theta$: a number $S_v$ picked from a Gaussian distribution with mean $\theta$ and standard deviation one. Then, in each discrete time iteration, each reveals its estimate of $\theta$ to its neighbors, and, observing its neighbors' actions, updates its belief using Bayes' Law. This process aggregates information efficiently, in the sense that all the agents converge to the belief that they would have, had they access to all the private measurements. We show that this process is computationally efficient, so that each agent's calculation can be easily carried out. We also show that on any graph the process converges after at most $2N \cdot D$ steps, where $N$ is the number of agents and $D$ is the diameter of the network. Finally, we show that on trees and on distance transitive-graphs the process converges after $D$ steps, and that it preserves privacy, so that agents learn very little about the private signal of most other agents, despite the efficient aggregation of information. Our results extend those in an unpublished manuscript of the first and last authors.Comment: Added coauthor. Added proofs for fast convergence on trees and distance transitive graphs. Also, now analyzing a notion of privac

Waterproof: educational software for learning how to write mathematical proofs

In order to help students learn how to write mathematical proofs, we developed the educational software called Waterproof (https://github.com/impermeable/waterproof). Waterproof is based on the Coq proof assistant. As students type out their proofs in the program, it checks the logical soundness of each proof step and provides additional guiding feedback. Contrary to Coq proofs, proofs written in Waterproof are similar in style to handwritten ones: proof steps are denoted using controlled natural language, the structure of proofs is made explicit by enforced signposting, and chains of inequalities can be used to prove larger estimates. To achieve this, we developed the Coq library coq-waterproof. The library extends Coq's default tactics using the Ltac2 tactic language. We include many code snippets in this article to increase the number of available Ltac2 examples. Waterproof has been used to supplement teaching the course Analysis 1 at the TU/e for a couple of years. Students started using Waterproof's controlled formulations of proof steps in their handwritten proofs as well; the explicit phrasing of these sentences helps to clarify the logical structure of their arguments.Comment: The Waterproof software can be found at https://github.com/impermeable/waterproof . This article pertains to Waterproof version 0.6.1. The Coq library coq-waterproof can be found at https://github.com/impermeable/coq-waterproof . This article pertains to coq-waterproof version 1.2.