空间位置关系的安全多方计算及其应用

空间位置关系的保密计算属于安全多方计算中的空间几何问题,在机密性商业、工程、军事等方面有着重要的意义。但目前大多数空间几何问题都是通过转化为距离或数据对应成比例问题解决的,计算复杂性较高,且应用范围受限。针对这些问题,该文先将原问题转化为一个点是否为一个方程的解,再利用一种简单高效的内积协议一次性解决了点线、点面、线线、线面、面面等5种空间位置关系的判定,并利用模拟范例证明了协议的安全性。该文方案并没有利用任何公钥加密算法,取得了信息论安全;并且由于问题的巧妙转化,使得能解决的问题更加广泛,效率也相对较高。     

两方有理数多重集的保密计算

集合的安全多方计算(SMC)在联合数据分析、敏感数据安全查询、数据可信交换等场景有着广泛的应用。该文基于有理数的几何编码,结合保密内积协议,首次提出了有理数域上两方多重集交集和并集的保密计算协议。应用模拟范例证明了协议在半诚实模型下的安全性,分别通过理论分析和仿真测试验证了协议的高效性。与现有协议相比,所设计协议无需给定包含所有集合元素的全集,可以保护集合势的隐私性,且在协议执行过程主要使用乘法运算,达到了信息论安全。     

Fast Two-Output Secure Computation with Optimal Error Probability

Cut-and-choose paradigm makes Yao's protocol for two-party computation secure in malicious model with an error probability.In CRYPTO 2013,based on multi-phase cut-and-choose,Lindell reduced this probability to the optimal value.However,this work can only compute single-output functions with optimal error probability.We transform multi-phase cut-and-choose for singleoutput case into one that can deal with two-output functions,meanwhile maintaining the optimal error probability.Based on this new paradigm,we propose an efficient two-output secure computation protocol.Besides,by utilizing the specific property of the output garbled keys,we solve the authenticity issue of the generator's output with only symmetric cryptographic operations linear in the output length of the generator,which is the most efficient method so far in standard model without Random oracle (Ro).     

安全两方向量优势统计协议及其应用

 安全两方向量优势统计问题是百万富翁问题的推广问题,用于两方在不泄漏自己保密向量信息的前提下统计出满足大于关系的分量的数目.本文在半诚实模型下利用加同态加密体制解决了安全两方向量优势统计问题,分析了该解决方案的正确性,安全性和复杂性;利用该优势统计协议设计了一个安全两方向量分量和排序协议,并且将设计的安全两方向量分量和排序协议应用于安全生成最小树图形算法中.     

思维进化计算及其应用

思维进化计算（Mind Evolutionary Computation,MEC）是一种新的进化计算方法。对MEC的系统结构、基本操作进行了概述,并剖析了MEC和遗传算法（Genetic Algorithms,GA）的不同之处,同时介绍了MEC的主要应用领域及研究成果。最后还对MEC今后的研究方向进行了探讨。     

Parallel Coin-Tossing and Constant-Round Secure Two-Party Computation

In this paper we show that any two-party functionality can be securely computed in a constant number of rounds , where security is obtained against (polynomial-time) malicious adversaries that may arbitrarily deviate from the protocol specification. This is in contrast to Yao's constant-round protocol that ensures security only in the face of semi-honest adversaries, and to its malicious adversary version that requires a polynomial number of rounds. In order to obtain our result, we present a constant-round protocol for secure coin-tossing of polynomially many coins (in parallel). We then show how this protocol can be used in conjunction with other existing constructions in order to obtain a constant-round protocol for securely computing any two-party functionality. On the subject of coin-tossing, we also present a constant-round almost perfect coin-tossing protocol, where by ``almost perfect' we mean that the resulting coins are guaranteed to be statistically close to uniform (and not just pseudorandom).     

有理区间的安全多方计算与应用

本文研究了有理数与有理区间的位置关系以及两个有理区间位置关系的安全多方计算.它们已广泛应用于数据库匹配、定位搜索等领域,是保密科学计算的一个重要分支.但目前已有文献在解决有理数与有理区间的位置关系时提出的协议效率较低,且两个有理区间位置关系问题的研究较为有限.针对这些问题,本文首先用多项式表示区间,将有理数与有理区间位置关系问题转化为整数向量的内积符号判定问题,设计了新的有理数与有理区间的保密计算协议.其次,以有理数与有理区间协议作为基础模块,设计了两个有理区间位置关系的保密计算协议.最后,理论分析及实验结果均表明本文方案是安全高效的,并给出了本文协议在有理数域上的百万富翁问题及计算几何问题的应用.     

最小值问题的安全多方计算及其应用

安全多方计算是国际密码学界近年来的研究热点.本文主要研究科学计算中最小值问题的安全多方计算,目前尚没有见到关于这个问题的解决方案.本文设计了一种新的编码方法,应用该编码方法和ElGamal乘法同态加密算法,并结合秘密分享以及门限密码体制,在半诚实模型下设计了三个能够抵抗合谋攻击的最小值安全多方计算方案,并应用模拟范例证明了方案的安全性.以最小值解决方案为基础还可以解决最大值安全计算以及并集的安全计算等科学计算问题.效率分析表明所设计的安全计算方案是高效的方案.     

集合成员关系的安全多方计算及其应用

集合成员关系的安全多方计算在保密数据挖掘和保密数据查询等方面有着重要的应用价值.针对以往方案在集合规模较大时的低效问题,本文将原问题转化成多项式一次性求值问题,在此基础上共设计了四个协议.利用同态加密设计了平凡协议1;利用离散对数设计了高效协议2,此协议非常简洁.最后,针对不同的应用场景又分别设计了云计算环境下外包用户计算的协议3和抗抵赖环境下可公开保密判定的协议4.通过分析和比较显示,我们的方案除了集合的势,其余任何信息都没有泄露,并且在集合规模较大时,相比以往方案高效而简洁.     

基于自动秩估计的黎曼优化矩阵补全算法及其在图像补全中的应用

矩阵补全(MC)作为压缩感知(CS)的推广,已广泛应用于不同领域。近年来,基于黎曼优化的MC算法因重构精度高、计算速度快的特点,引起了广泛关注。针对基于黎曼优化的MC算法需假设原矩阵秩固定已知,且随机选择迭代起点的特点,该文提出一种基于自动秩估计的黎曼优化MC算法。该算法通过优化包含秩正则项的目标函数,迭代获取秩估计值和预重构矩阵。在估计所得秩对应的矩阵空间上以预重构矩阵为迭代起点,利用基于黎曼流形的共轭梯度法进行矩阵补全,从而提高重构精度。实验结果表明,与几种经典的图像补全方法相比,该文算法图像重构精度显著提高。     

简单网络管理协议的研究与应用

通过对简单网络管理协议（SNMP）的通讯原理、消息结构及抽象语法表示（ASN.1）、BER编码的分析,并对RFC1213管理信息库（MIB-Ⅱ）和用户数据报协议（UDP）的分析研究,利用WindowsSNMP提供的服务,使用C＋＋Builder面向对象开发工具进行编程,实现SNMP协议包的构造和协议包的解析,提供网络设备管理的简便方式,从而保证网络设备稳定运行。     

A New Weighted Least Squares Support Vector Machines and Its Sequential Minimal Optimization Algorithm

Least squares support vector machines （LSSVM） is widely used in pattern recognition and artificial intelligence domain in recent years for its efficiency in classification and regression. The solution of LSSVM is an optimization problem of a Sum squared error （SSE） cost function with only equality constraints and can be solved in a simple linear system. However, its generalization performance is sensitive to noise points and outliers that are often existent in training dataset. In order to endow robustness to LSSVM, a new method for computing weight vector of error is proposed and the substituting of weighted error vector for original error vector in LSSVM gives birth to a new weighted LSSVM. The method gets weight factor by computing distance between sample and its corresponding class center. Sequential minimal optimization （SMO） algorithm is also extended to the new method for its efficient application. Comparison experiments show superiority of the new method in terms of generalization performance, robust property and sparse approximation. Especially, the new method is much faster than the other method for large number of samples.     

PCNN Model Analysis and Its Automatic Parameters Determination in Image Segmentation and Edge Detection

The Pulse coupled neural network （PCNN） has been widely used in digital image processing, but the automatic parameters determination is still a difficult as- pect, which becomes the focus of PENN research. In this paper, by the classical solution to difference equations and the time-domain analysis of PCNN model, we provide the expressions of the firing time and the firing period of neu- rons, and reveal the “mathematics firing” phenomenon of PCNN. Based on this, we propose a new method of auto- matic parameters determination based on both eliminating the “mathematics firing” and getting the highest efficiency of PCNN. We also present an edge detection model on the basis of image segmentation of PCNN and a method to determine automatically the parameters of the model. Ex- perimental results prove the validity and efficiency of our proposed algorithm for the segmentation and the edge de- tection of the test images.