Secure Multi-Party Computation without Agreement

It has recently been shown that authenticated Byzantine agreement, in which more than a third of the parties are corrupted, cannot be securely realized under concurrent or parallel (stateless) composition. This result puts into question any usage of authenticated Byzantine agreement in a setting where many executions take place. In particular, this is true for the whole body of work of secure multi-party protocols in the case that a third or more of the parties are corrupted. This is because these protocols strongly rely on the extensive use of a broadcast channel, which is in turn realized using authenticated Byzantine agreement. We remark that it was accepted folklore that the use of a broadcast channel (or authenticated Byzantine agreement) is actually essential for achieving meaningful secure multi-party computation whenever a third or more of the parties are corrupted. In this paper we show that this folklore is false. We present a mild relaxation of the definition of secure computation allowing abort. Our new definition captures all the central security issues of secure computation, including privacy, correctness and independence of inputs. However, the novelty of the definition is in decoupling the issue of agreement from these issues. We then show that this relaxation suffices for achieving secure computation in a point-to-point network. That is, we show that secure multi-party computation for this definition can be achieved for any number of corrupted parties and without a broadcast channel (or trusted pre-processing phase as required for running authenticated Byzantine agreement). Furthermore, this is achieved by just replacing the broadcast channel in known protocols with a very simple and efficient echo-broadcast protocol. An important corollary of our result is the ability to obtain multi-party protocols that remain secure under composition, without assuming a broadcast channel.     

Automated synthesis of multiple analog circuits using evolutionary computation for redundancy-based fault-tolerance

Analog circuits are one of the most important parts of modern electronic systems and the failure of electronic hardware presents a critical threat to the completion of modern aircraft, spacecraft, and robot missions. Compared to digital circuits, designing fault-tolerant analog circuits is a difficult and knowledge-intensive task. A simple but powerful method for robustness is a redundancy approach to use multiple circuits instead of single one. For example, if component failures occur, other redundant components can replace the functions of broken parts and the system can still work. However, there are several research issues to make the redundant system automatically. In this paper, we used evolutionary computation to generate multiple analog circuits automatically and then we combined the solutions to generate robust outputs. Evolutionary computation is a natural way to produce multiple redundant solutions because it is a population-based search. Experimental results on the evolution of the low-pass, high-pass and band-stop filters show that the combination of multiple evolved analog circuits produces results that are more robust than those of the best single circuit.     

Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives

The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data, various privacy-preserving schemes have been introduced in the recent past. However, technological advancement in CPSSs requires the modifications of previous techniques to suit its dynamics. Meanwhile, differential privacy has emerged as an effective method to safeguard CPSSs data privacy. To completely comprehend the state-of-the-art developments and learn the field’s research directions, this article provides a comprehensive review of differentially private data fusion and deep learning in CPSSs. Additionally, we present a novel differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems , and various future research directions for CPSSs.     

高效动态存储再分配方案与实现

为提高大小和数量都不确定的数据动态存储的空间利用率和处理速度,针对常规系统动态存储分配存在的问题,提出一种高效的动态存储再分配方案。通过减少系统动态存储分配的执行次数,在有效提高存储空间利用率的同时,提高存储空间动态分配的速度,给出动态存储再分配方案的具体实现算法,用实际数据对其性能进行测试。测试结果表明,该方案能节省36％至75％的系统存储空间,分配速度提高了20至50倍。     

Coordinate descent with arbitrary sampling I: algorithms and complexity†

We study the problem of minimizing the sum of a smooth convex function and a convex block-separable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literature before for this problem. ALPHA is a very flexible algorithm: in special cases, it reduces to deterministic and randomized methods such as gradient descent, coordinate descent, parallel coordinate descent and distributed coordinate descent—both in nonaccelerated and accelerated variants. The variants with arbitrary (or importance) sampling are new. We provide a complexity analysis of ALPHA, from which we deduce as a direct corollary complexity bounds for its many variants, all matching or improving best known bounds.     

A note about the complexity of minimizing Nesterov's smooth Chebyshev–Rosenbrock function

This short note considers and resolves the apparent contradiction between known worst-case complexity results for first- and second-order methods for solving unconstrained smooth nonconvex optimization problems and a recent note by Jarre [On Nesterov's smooth Chebyshev–Rosenbrock function, Optim. Methods Softw. (2011)] implying a very large lower bound on the number of iterations required to reach the solution's neighbourhood for a specific problem with variable dimension.     

Aesthetics and complexity in digital layout systems

Abstract

The layout of pages which carry information is the bread and butter of the graphic design profession. The rationale of what elements are chosen and where they are placed is based on tacit knowledge gained over a number of years of experience. The aesthetics associated with the overall finished design are often based on a style. Important elements of style are complexity and aesthetic value. In this paper I explore how various mathematical aesthetic functions can use complexity as an arousal enhancing or limiting mechanism in the application of layout designs.     

Rigidity of a simple extended lower triangular matrix

For the all-ones lower triangular matrices, the upper and lower bounds on rigidity are known to match [P. Pudlak, Z. Vavrin, Computation of rigidity of order n²/r for one simple matrix, Comment Math. Univ. Carolin. 32 (2) (1991) 213-218]. In this short note, we apply these techniques to the all-ones extended lower triangular matrices, to obtain upper and lower bounds with a small gap between the two; we show that the rigidity is .     

Rollout sampling approximate policy iteration

Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.