Arya: Nearly linear-time zero-knowledge proofs for correct program execution

There have been tremendous advances in reducing interaction, communication and verification time in zero-knowledge proofs but it remains an important challenge to make the prover efficient. We construct the first zero-knowledge proof of knowledge for the correct execution of a program on public and private inputs where the prover computation is nearly linear time. This saves a polylogarithmic factor in asymptotic performance compared to current state of the art proof systems. We use the TinyRAM model to capture general purpose processor computation. An instance consists of a TinyRAM program and public inputs. The witness consists of additional private inputs to the program. The prover can use our proof system to convince the verifier that the program terminates with the intended answer within given time and memory bounds. Our proof system has perfect completeness, statistical special honest verifier zero-knowledge, and computational knowledge soundness assuming linear-time computable collision-resistant hash functions exist. The main advantage of our new proof system is asymptotically efficient prover computation. The prover’s running time is only a superconstant factor larger than the program’s running time in an apples-to-apples comparison where the prover uses the same TinyRAM model. Our proof system is also efficient on the other performance parameters; the verifier’s running time and the communication are sublinear in the execution time of the program and we only use a log-logarithmic number of rounds

Brief report: how adolescents with ASD process social information in complex scenes. Combining evidence from eye movements and verbal descriptions

We investigated attention, encoding and processing of social aspects of complex photographic scenes. Twenty-four high-functioning adolescents (aged 11–16) with ASD and 24 typically developing matched control participants viewed and then described a series of scenes, each containing a person. Analyses of eye movements and verbal descriptions provided converging evidence that both groups displayed general interest in the person in each scene but the salience of the person was reduced for the ASD participants. Nevertheless, the verbal descriptions revealed that participants with ASD frequently processed the observed person’s emotion or mental state without prompting. They also often mentioned eye-gaze direction, and there was evidence from eye movements and verbal descriptions that gaze was followed accurately. The combination of evidence from eye movements and verbal descriptions provides a rich insight into the way stimuli are processed overall. The merits of using these methods within the same paradigm are discussed

Aurora: Transparent Succinct Arguments for R1CS

We design, implement, and evaluate a zkSNARK for Rank-1 Constraint Satisfaction (R1CS), a widely-deployed NP-complete language that is undergoing standardization. Our construction uses a transparent setup, is plausibly post-quantum secure, and uses lightweight cryptography. A proof attesting to the satisfiability of n constraints has size $O(\log^2 n)$; it can be produced with $O(n \log n)$ field operations and verified with $O(n)$. At 128 bits of security, proofs are less than 130kB even for several million constraints, more than 20x shorter than prior zkSNARK with similar features. A key ingredient of our construction is a new Interactive Oracle Proof (IOP) for solving a *univariate* analogue of the classical sumcheck problem [LFKN92], originally studied for *multivariate* polynomials. Our protocol verifies the sum of entries of a Reed--Solomon codeword over any subgroup of a field. We also provide libiop, an open-source library for writing IOP-based arguments, in which a toolchain of transformations enables programmers to write new arguments by writing simple IOP sub-components. We have used this library to specify our construction and prior ones

Fractal: Post-Quantum and Transparent Recursive Proofs from Holography

We present a new methodology to efficiently realize recursive composition of succinct non-interactive arguments of knowledge (SNARKs). Prior to this work, the only known methodology relied on pairing-based SNARKs instantiated on cycles of pairing-friendly elliptic curves, an expensive algebraic object. Our methodology does not rely on any special algebraic objects and, moreover, achieves new desirable properties: it is *post-quantum* and it is *transparent* (the setup is public coin). We exploit the fact that recursive composition is simpler for SNARKs with *preprocessing*, and the core of our work is obtaining a preprocessing zkSNARK for rank-1 constraint satisfiability (R1CS) that is post-quantum and transparent. We obtain this latter by establishing a connection between holography and preprocessing in the random oracle model, and then constructing a holographic proof for R1CS. We experimentally validate our methodology, demonstrating feasibility in practice

Stacked Garbling for Disjunctive Zero-Knowledge Proofs

Zero-knowledge (ZK) proofs receive wide attention, especially with respect to non-interactivity, small proof size, and fast verification. We instead focus on fast total proof time, in particular for large Boolean circuits. Under this metric, Garbled Circuit (GC)-based ZK, originally proposed by Jawurek et al. ([JKO], CCS 2013), remains state-of-the-art due to the low-constant linear scaling of garbling. We improve GC-ZK for proof statements with conditional clauses. Our communication is proportional to the longest clause rather than to the entire proof statement. This is most useful when the number of branches $m$ is large, resulting in up to $m\times$ communication improvement over JKO. In our proof-of-concept illustrative application, the prover demonstrates knowledge of a bug in a codebase consisting of any number of snippets of C code. Our computation cost is linear in the size of the codebase and communication is constant in the number of snippets. That is, we require only enough communication for the single largest snippet! Our conceptual contribution is stacked garbling for ZK, a privacy-free circuit garbling scheme that, when used with the JKO GC-ZK protocol, constructs efficient ZK proofs. Given a Boolean circuit $C$ and computational security parameter $\kappa$, our garbling is $L\kappa$ bits long, where $L$ is the length of the longest execution path in $C$. All prior concretely efficient garbling schemes produce garblings of size $|C|\kappa$. The computational cost of our scheme is not increased over prior state-of-the-art. We implemented our technique and demonstrate significantly improved performance. For functions with branching factor $m$, we improve communication by $m\times$ compared to JKO. Compared with recent systems (STARK, Libra, KKW, Ligero, Aurora, Bulletproofs), our scheme offers better proof times for large circuits: $35-1000\times$ or more, depending on circuit size and on the compared scheme. For our illustrative application, we consider four C code snippets. Each snippet has 30-50 LOC; one snippet allows an invalid memory dereference. The entire proof takes 0.15 seconds and communicates 1.5 MB

Zero Knowledge Protocols from Succinct Constraint Detection

We study the problem of constructing proof systems that achieve both soundness and zero knowledge unconditionally (without relying on intractability assumptions). Known techniques for this goal are primarily *combinatorial*, despite the fact that constructions of interactive proofs (IPs) and probabilistically checkable proofs (PCPs) heavily rely on *algebraic* techniques to achieve their properties. We present simple and natural modifications of well-known algebraic IP and PCP protocols that achieve unconditional (perfect) zero knowledge in recently introduced models, overcoming limitations of known techniques. 1. We modify the PCP of Ben-Sasson and Sudan [BS08] to obtain zero knowledge for NEXP in the model of Interactive Oracle Proofs [BCS16,RRR16], where the verifier, in each round, receives a PCP from the prover. 2. We modify the IP of Lund, Fortnow, Karloff, and Nisan [LFKN92] to obtain zero knowledge for #P in the model of Interactive PCPs [KR08], where the verifier first receives a PCP from the prover and then interacts with him. The simulators in our zero knowledge protocols rely on solving a problem that lies at the intersection of coding theory, linear algebra, and computational complexity, which we call the *succinct constraint detection* problem, and consists of detecting dual constraints with polynomial support size for codes of exponential block length. Our two results rely on solutions to this problem for fundamental classes of linear codes: * An algorithm to detect constraints for Reed--Muller codes of exponential length. This algorithm exploits the Raz--Shpilka [RS05] deterministic polynomial identity testing algorithm, and shows, to our knowledge, a first connection of algebraic complexity theory with zero knowledge. * An algorithm to detect constraints for PCPs of Proximity of Reed--Solomon codes [BS08] of exponential degree. This algorithm exploits the recursive structure of the PCPs of Proximity to show that small-support constraints are locally spanned by a small number of small-support constraints

A behavioral comparison of male and female adults with high functioning autism spectrum conditions

Autism spectrum conditions (ASC) affect more males than females in the general population. However, within ASC it is unclear if there are phenotypic sex differences. Testing for similarities and differences between the sexes is important not only for clinical assessment but also has implications for theories of typical sex differences and of autism. Using cognitive and behavioral measures, we investigated similarities and differences between the sexes in age- and IQ-matched adults with ASC (high-functioning autism or Asperger syndrome). Of the 83 (45 males and 38 females) participants, 62 (33 males and 29 females) met Autism Diagnostic Interview-Revised (ADI-R) cut-off criteria for autism in childhood and were included in all subsequent analyses. The severity of childhood core autism symptoms did not differ between the sexes. Males and females also did not differ in self-reported empathy, systemizing, anxiety, depression, and obsessive-compulsive traits/symptoms or mentalizing performance. However, adult females with ASC showed more lifetime sensory symptoms (p = 0.036), fewer current socio-communication difficulties (p = 0.001), and more self-reported autistic traits (p = 0.012) than males. In addition, females with ASC who also had developmental language delay had lower current performance IQ than those without developmental language delay (p<0.001), a pattern not seen in males. The absence of typical sex differences in empathizing-systemizing profiles within the autism spectrum confirms a prediction from the extreme male brain theory. Behavioral sex differences within ASC may also reflect different developmental mechanisms between males and females with ASC. We discuss the importance of the superficially better socio-communication ability in adult females with ASC in terms of why females with ASC may more often go under-recognized, and receive their diagnosis later, than males

Sonic: Zero-Knowledge SNARKs from Linear-Size Universal and Updateable Structured Reference Strings

Ever since their introduction, zero-knowledge proofs have become an important tool for addressing privacy and scalability concerns in a variety of applications. In many systems each client downloads and verifies every new proof, and so proofs must be small and cheap to verify. The most practical schemes require either a trusted setup, as in (pre-processing) zk-SNARKs, or verification complexity that scales linearly with the complexity of the relation, as in Bulletproofs. The structured reference strings required by most zk-SNARK schemes can be constructed with multi-party computation protocols, but the resulting parameters are specific to an individual relation. Groth et al. discovered a zk-SNARK protocol with a universal structured reference string that is also updatable, but the string scales quadratically in the size of the supported relations. Here we describe a zero-knowledge SNARK, Sonic, which supports a universal and continually updatable structured reference string that scales linearly in size. We also describe a generally useful technique in which untrusted "helpers" can compute advice that allows batches of proofs to be verified more efficiently. Sonic proofs are constant size, and in the "helped" batch verification context the marginal cost of verification is comparable with the most efficient SNARKs in the literature