Coin Tossing is Strictly Weaker Than Bit Commitment

We define cryptographic assumptions applicable to two mistrustful parties who each control two or more separate secure sites between which special relativity guarantees a time lapse in communication. We show that, under these assumptions, unconditionally secure coin tossing can be carried out by exchanges of classical information. We show also, following Mayers, Lo and Chau, that unconditionally secure bit commitment cannot be carried out by finitely many exchanges of classical or quantum information. Finally we show that, under standard cryptographic assumptions, coin tossing is strictly weaker than bit commitment. That is, no secure classical or quantum bit commitment protocol can be built from a finite number of invocations of a secure coin tossing black box together with finitely many additional information exchanges.Comment: Final version; to appear in Phys. Rev. Let

Towards Human Computable Passwords

An interesting challenge for the cryptography community is to design authentication protocols that are so simple that a human can execute them without relying on a fully trusted computer. We propose several candidate authentication protocols for a setting in which the human user can only receive assistance from a semi-trusted computer --- a computer that stores information and performs computations correctly but does not provide confidentiality. Our schemes use a semi-trusted computer to store and display public challenges $C_i\in[n]^k$. The human user memorizes a random secret mapping $\sigma:[n]\rightarrow\mathbb{Z}_d$ and authenticates by computing responses $f(\sigma(C_i))$ to a sequence of public challenges where $f:\mathbb{Z}_d^k\rightarrow\mathbb{Z}_d$ is a function that is easy for the human to evaluate. We prove that any statistical adversary needs to sample $m=\tilde{\Omega}(n^{s(f)})$ challenge-response pairs to recover $\sigma$, for a security parameter $s(f)$ that depends on two key properties of $f$. To obtain our results, we apply the general hypercontractivity theorem to lower bound the statistical dimension of the distribution over challenge-response pairs induced by $f$ and $\sigma$. Our lower bounds apply to arbitrary functions $f$ (not just to functions that are easy for a human to evaluate), and generalize recent results of Feldman et al. As an application, we propose a family of human computable password functions $f_{k_1,k_2}$ in which the user needs to perform $2k_1+2k_2+1$ primitive operations (e.g., adding two digits or remembering $\sigma(i)$), and we show that $s(f) = \min\{k_1+1, (k_2+1)/2\}$. For these schemes, we prove that forging passwords is equivalent to recovering the secret mapping. Thus, our human computable password schemes can maintain strong security guarantees even after an adversary has observed the user login to many different accounts.Comment: Fixed bug in definition of Q^{f,j} and modified proofs accordingl

Verbal Learning and Memory After Cochlear Implantation in Postlingually Deaf Adults: Some New Findings with the CVLT-II

OBJECTIVES: Despite the importance of verbal learning and memory in speech and language processing, this domain of cognitive functioning has been virtually ignored in clinical studies of hearing loss and cochlear implants in both adults and children. In this article, we report the results of two studies that used a newly developed visually based version of the California Verbal Learning Test-Second Edition (CVLT-II), a well-known normed neuropsychological measure of verbal learning and memory. DESIGN: The first study established the validity and feasibility of a computer-controlled visual version of the CVLT-II, which eliminates the effects of audibility of spoken stimuli, in groups of young normal-hearing and older normal-hearing (ONH) adults. A second study was then carried out using the visual CVLT-II format with a group of older postlingually deaf experienced cochlear implant (ECI) users (N = 25) and a group of ONH controls (N = 25) who were matched to ECI users for age, socioeconomic status, and nonverbal IQ. In addition to the visual CVLT-II, subjects provided data on demographics, hearing history, nonverbal IQ, reading fluency, vocabulary, and short-term memory span for visually presented digits. ECI participants were also tested for speech recognition in quiet. RESULTS: The ECI and ONH groups did not differ on most measures of verbal learning and memory obtained with the visual CVLT-II, but deficits were identified in ECI participants that were related to recency recall, the buildup of proactive interference, and retrieval-induced forgetting. Within the ECI group, nonverbal fluid IQ, reading fluency, and resistance to the buildup of proactive interference from the CVLT-II consistently predicted better speech recognition outcomes. CONCLUSIONS: Results from this study suggest that several underlying foundational neurocognitive abilities are related to core speech perception outcomes after implantation in older adults. Implications of these findings for explaining individual differences and variability and predicting speech recognition outcomes after implantation are discussed

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains

BRUNO: A Deep Recurrent Model for Exchangeable Data

We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provably exchangeable, meaning that the joint distribution over observations is invariant under permutation: this property lies at the heart of Bayesian inference. The model does not require variational approximations to train, and new samples can be generated conditional on previous samples, with cost linear in the size of the conditioning set. The advantages of our architecture are demonstrated on learning tasks that require generalisation from short observed sequences while modelling sequence variability, such as conditional image generation, few-shot learning, and anomaly detection.Comment: NIPS 201