The emergence of quantum computing has provided new paradigms for cryptography. On the one hand, it poses significant new threats to existing classically cryptographic systems, requiring the community to define new security models that capture what a quantum adversary can do. On the other hand, it gives us new tools to design cryptographic protocols, with weaker assumptions than in the classical world, or even protocols that are impossible classically. In this survey, we first give an overview of new security definitions for classical cryptography, considering quantum adversaries who can either only use local quantum computation (post-quantum security), or even send quantum messages and in particular have access to oracle in superposition (quantum security). We explore these new notions through the examples of commitments, zero-knowledge proofs, encryption, and signatures. Then, we present what is arguably the most famous application of quantum cryptography: quantum key distribution (QKD) protocols that take advantage of unique properties of quantum mechanics to provide secure communication unconditionally. We also explore cryptography beyond QKD, focusing on unclonable cryptography: a family of cryptographic functionalities, built with quantum states, and designed to be resistant to counterfeit by leveraging the "no-cloning" theorem. We examine in particular quantum money, but also the recent notions of unclonable encryption and copy-protection, including related variants. By presenting a comprehensive survey of these topics, this paper aims to provide a thorough understanding of the current landscape and future potential of quantum cryptography.
A contract is an economic tool used by a principal to incentivize one or more agents to exert effort on her behalf, by defining payments based on observable performance measures. A key challenge addressed by contracts — known in economics as moral hazard — is that, absent a properly set up contract, agents might engage in actions that are not in the principal’s best interest. Another common feature of contracts is limited liability, which means that payments can go only from the principal — who has the deep pocket — to the agents. With classic applications of contract theory moving online, growing in scale, and becoming more data-driven, tools from contract theory become increasingly important for incentiveaware algorithm design. At the same time, algorithm design offers a whole new toolbox for reasoning about contracts, ranging from additional tools for studying the tradeoff between simple and optimal contracts, through a language for discussing the computational complexity of contracts in combinatorial settings, to a formalism for analyzing data-driven contracts. This survey aims to provide a computer science-friendly introduction to the basic concepts of contract theory.We give an overview of the emerging field of “algorithmic contract theory” and highlight work that showcases the potential for interaction between the two areas. We also discuss avenues for future research.
This is a survey of unconditional pseudorandom generators (PRGs). A PRG uses a short, truly random seed to generate a long, "pseudorandom" sequence of bits. To be more specific, for each restricted model of computation (e.g., bounded-depth circuits or read-once branching programs), we would like to design a PRG that "fools" the model, meaning that every function computable in the model behaves approximately the same when we plug in pseudorandom bits from the PRG as it does when we plug in truly random bits. In this survey, we discuss four major paradigms for designing PRGs: center dot We present several PRGs based on k-wise uniform generators, small-bias generators, and simple combinations thereof, including proofs of Viola's theorem on fooling low-degree polynomials [242] and Braverman's theorem on fooling AC(0) circuits [36]. center dot We present several PRGs based on "recycling" random bits to take advantage of communication bottlenecks, such as the Impagliazzo-Nisan-Wigderson generator [131]. center dot We present connections between PRGs and computational hardness, including the Nisan-Wigderson framework for converting a hard Boolean function into a PRG [183]. center dot We present PRG frameworks based on random restrictions, including the "polarizing random walks" framework [49]. We explain how to use these paradigms to construct PRGs that work unconditionally, with no unproven complexity-theoretic assumptions. The PRG constructions use ingredients such as finite field arithmetic, expander graphs, and randomness extractors. The analyses use techniques such as Fourier analysis, sandwiching approximators, and simplification-under-restrictions lemmas.
Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms. Central to this drive is the appeal of engineering a computational system that both discovers and deploys high-performance solutions to arbitrary ML problems with minimal human interaction. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. However, these ambitions are unlikely to be achieved in a robust manner without the broader synthesis of various mechanisms and theoretical frameworks, which, at the present time, remain scattered across numerous research threads. Accordingly, this review seeks to motivate a more expansive perspective on what constitutes an automated/autonomous ML system, alongside consideration of how best to consolidate those elements. In doing so, we survey developments in the following research areas: hyperparameter optimisation, multi-component models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. We also develop a conceptual framework throughout the review, augmented by each topic, to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. Ultimately, we conclude that the notion of architectural integration deserves more discussion, without which the field of automated ML risks stifling both its technical advantages and general uptake.
Traditional computer science is Boolean: a Turing machine accepts or rejects its input, and logic assertions are true or false. A primary use of logic in computer science has been the specification and verification of reactive systems. There, desired behaviors of systems are formally specified by temporal-logic formulas, and questions about systems and their behaviors are reduced to questions like satisfiability and model checking. While correctness is binary, many questions we want to ask about systems are multi-valued. The multi-valued setting arises directly in systems with quantitative aspects, for example systems with fuzzy assignments or stochastic dynamics, and arises also in Boolean systems, where it origins from the semantics of the specification formalism. In particular, beyond checking whether a system satisfies its specification, we may want to evaluate the quality in which the specification is satisfied. The term "quality" may refer to many aspects of the behavior: we may want to prioritize different satisfaction alternatives, refer to delays, costs, and many more. In recent years, we have seen a growing effort in the formal-method community to shift from Boolean specification formalisms to multi-valued ones. The shift involves a development of multi-valued temporal logics as well as algorithms and tools for reasoning about such logics. This survey describes the basics of specification and verification of reactive systems, and the automata-theoretic approach for them: by translating temporal-logic formulas to automata, one reduces questions like satisfiability and model checking to decision problems on automata, like non-emptiness and language containment. We first describe the Boolean setting: temporal logics, and their applications in specification and verification. Since we care about on-going behaviors of non-terminating systems, the formalisms we study specify infinite computations, and we focus on the theoretical properties of automata on infinite words. The transition from finite to infinite words results in a beautiful mathematical model with much richer combinatorial properties. We then describe two multi-valued settings. The first is based on finite lattices and the second on arbitrary functions over [0, 1]. In both settings, the goal is to refine the Boolean correctness query to a quantitative-evaluation query. Accordingly, the formalisms we introduce are such that the satisfaction value of a temporal-logic formula in a model, or the membership value of a word in the language of an automaton, are multi valued, and classical decision problems become search problems.
