偏振旋转的量子私有信息检索方案

私有信息检索是安全多方计算的重要问题。传统对称私有信息检索(SPIR)的很多假设在量子信息机制下非常脆弱,其安全性受到挑战。目前已提出的量子私有信息检索大都不易实施,该文提出基于偏振旋转的对称量子私有信息检索协议和实验方案。实验方案利用单光子的偏振旋转产生量子密文,不需要复杂的计算,便于硬件实现。协议的无条件安全性由量子力学Heisenberg测不准原理及不可克隆原理保证,并增加了用户诚实性检测,在所提出的非诚实合作模型下,非诚实用户的恶意行为不会造成隐私的泄露,在安全性、鲁棒性、抗第三方窃听等方面均优于经典环境的多种方案。     

基于量子中心的测量型量子保密求和协议

量子保密求和是量子安全计算的基础,目的是在保护参与者私有信息的前提下求出参与者秘密信息的和。提出一个基于GHZ类态的三方量子保密求和协议,其中只有量子中心拥有全量子能力,其余参与者只能对接收的量子态进行反射或测量。理论分析表明,所提出的协议可以确保正确性,即多个参与者最后可以成功计算他们秘密的和;同时,该协议还可以抵抗参与者攻击和外部攻击,即无论是外部攻击者还是内部参与者都不能获得除自己的秘密与结果之外的任何信息。最后,进一步讨论了如何将协议的参与者由三方拓展至多方。     

基于压缩态的连续变量量子对话协议

在量子密码协议中,比较典型的量子密钥分配协议（QKD）和量子安全直接通信（QSDC）协议都只能实现信息的单向传输。而在量子对话协议中,通信双方Alice和Bob只需要一次通信便可以同时交换秘密信息。目前流行的量子对话协议都是基于离散变量实现,缺点是受到现有技术水平的限制,实用性不强,而连续变量的量子协议可以解决这个问题。基于连续变量提出了一个新的量子对话协议,采用信息论的方法对协议进行分析,结果表明该协议不仅安全,而且信道容量高于离散变量量子对话协议。     

基于非对称QKD的股票交易数据库隐私查询协议

证券交易所的股票交易数据库中包含大量敏感信息,用户查询该数据库时保障账户及数据库的隐私十分重要.分析了非对称量子密钥分配(QKD)及其优良特性,提出了一种基于非对称QKD的不经意集合元素映射判定协议.该协议在查询数据库时保证了用户和数据库的隐私.安全性分析结果表明,该协议能有效抵抗量子存储攻击、伪造量子态攻击和纠缠测量攻击,具有很高的信道损耗容忍度.     

高效的量子有序多重签名协议

基于量子一次一密和量子密钥分配,提出了一种针对经典消息的量子有序多重签名协议。不同于已有的量子有序多重签名协议,协议不需要使用多粒子纠缠态,只需要对单粒子实施幺正操作就能完成签名。并且协议具有很高的灵活性,对签名者的加入和删除能够很容易实现。协议的安全性依赖于无条件安全的量子密钥分配,现有的各种攻击方式被证明对本协议也是无效的。此外,与其他主流的量子有序多重签名协议相比较,协议的签名过程和验证过程的效率都有较大的提高。     

自由空间量子密钥分配协议研究

针对自由空间量子信道特点,对已有量子密钥分配协议加以改进,提出自由空间量子密钥分配协议。将瞄准捕获跟踪和单光子捕获加入到协议之中,使得自由空间量子密钥分配协议更加完善、安全。     

针对经典消息的高效量子签名协议

基于量子一次一密和量子密钥分配,提出了一种针对经典消息的高效量子签名协议。该协议利用Hash函数对经典消息取摘要,将任意位的经典消息编码为确定位数的量子信息,在对位数较多的经典信息签名时,大大减少了密钥长度和所需传输的量子位。协议不需要使用纠缠态,只需要进行von Neumann测量。与现有的几种量子签名协议相比,协议效率显著提高。     

多用户网络环境下量子密码术

在传统的点到点之间进行量子密钥分发协议的基础上,利用量子存储技术和EPR粒子纠缠态互换的方法,提出了在多用户、多控制中心、远距离的网络环境下进行量子密钥传送的方案。与传统的点与点之间的量子密钥传送协议类似,其安全性也是建立在量子力学原理上,任何窃听者的存在必将使生成密钥的误码率上升而被合法通信用户发现。     

基于腔QED任意两个Bell态纠缠交换的量子隐写协议

根据腔量子电动力学(QED,quantum electrodynamics)原子的演化规律提出腔QED内新颖的量子隐写协议,其隐藏容量高达4bit。协议通过腔QED中任意两个Bell态纠缠交换,建立一个隐藏信道传送秘密信息。协议不需将多粒子量子纠缠态作为量子资源,也不涉及关于多粒子量子纠缠态的纠缠交换和量子测量。分析表明,本文协议能够抵抗截获-重发攻击、测量-重发攻击和纠缠-测量攻击,具有良好的安全性。     

基于密钥托管的量子加密网络

量子信息论的研究是量子理论和经典信息论的一个重要交叉领域，量子位不可复制且量子纠缠态不可区分的量子信息特征提供了一种理论上绝对安全的加密术一量子加密。本文对量子必钥分发协议进行了研究，利用点点间量子密钥分发协议和基于传统密钥托管方案，提出多用户、多控制中心网络的环境下量子密钥的分发过程。     

多服务器环境下的隐私信息检索

隐私信息检索（PIR）是为了保障个人隐私在公共网络平台上的私密性而采用的一种阻止数据库知晓用户查询信息的策略。通过研究已经被提出的PIR方案,分析其优缺点,试图探求一种能够综合考虑多方面安全性的可行PIR方案模型。文章采用Shamir秘密共享协议、乘法同态加密和其他相关的技术,结合当前网络环境的复杂性,并考虑可能存在的对用户检索信息的恶意攻击,设计了一种基于多服务器的隐私信息检索方案。     

Guaranteed, reliable, secure broadcast networks

A patent-pending invention for handling reliable and secure broadcasts is presented. This invention guarantees all computers in the network the reception of all broadcast messages in the proper sequence, even though there may be line noise, hardware malfunction, or busy receivers. This makes possible correct distributed database updates, parallel information retrieval, and many other applications. The invention adds four specialized components to existing networks and uses a special communication protocol. It can be applied to both local area and wide area networks     

基才时间信息的快速检索方法探究

本文研究了利用时间信息实现快速检索视频文件的方法,视频文件的压缩编码标准采用的是H．264,该方法主要包括视频分析和视频检索两个部分的研究。在视频分析部分,对码流中的IDR帧（关键帧）进行抓取和获得其时间、位置、大小等信息。并将得到的时间等信息存入至数据库。该部分的重点是时间信息的获取,由于负责传输码流的RTP数据分组的时间戳记录了码流的时间信息,所以着重描述了获取并且记录RTP时间戳的过程。视频检索部分,利用时间等查询条件从数据库中进行视频检索。对所述方法进行研究。结果表明使用该方法能够快速查看某时刻视频内容．避免了检索视频时的重复处理。     

A similarity learning approach to content-based image retrieval: application to digital mammography

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.     

Content-based retrieval of dynamic PET functional images

The recent information explosion has led to a massively increased demand for multimedia data storage in integrated database systems. Content-based retrieval is an important alternative and complement to traditional keyword-based searching for multimedia data and can greatly enhance information management. However, current content-based image retrieval techniques have some deficiencies when applied in the biomedical functional imaging domain. In this paper, we presented a prototype design for a content-based functional image retrieval database system for dynamic positron emission tomography (PET). The system supports efficient content-based retrieval based on physiological kinetic features and reduces image storage requirements. This design makes it possible to maintain a large number of patient data sets online and to rapidly retrieve dynamic functional image sequences for the interpretation and generation of physiological parametric images, and offers potential advantages in medical image data management and telemedicine, as well as providing possible opportunities in the statistical and comparative analysis of functional image data     

基于W态的量子秘密共享协议

基于W态及其非定域纠缠关联性,利用量子远程通信设计了一种量子秘密共享协议。在该协议中,Alice制备三粒子W态及秘密量子信息,将W态中的任意两粒子分别发送给Bob1和Bob2,并对自己拥有的粒子进行Bell基联合测量;依据Alice的测量结果,Bob1和Bob2联合进行相应的局域操作就能共同得到秘密信息。并对协议的安全性进行了详细分析,研究表明该协议能抵御多种攻击,如干扰重发攻击、纠缠攻击等。     

基于“点到面”策略的图像检索

图像检索是计算机视觉领域的一个重要分支。其主要目的是从图像数据库中找出与查询图像相似的语义图像。传统的图像检索方法是在查询图像和数据库图像之间进行“点到点”检索。但是,单个查询图像包含的类别提示较少,即类别信息较弱,使得检索结果并不理想。为了解决这个问题,本文提出了一种基于“点到面”的类别检索策略来扩展一个图像（点）到一个图像类别（面）,这意味着从单个查询图像到整个图像类别的语义扩展。该方法挖掘了查询图像的类别信息。在两个常用的数据集上对所提出方法的性能进行了评估。实验表明,该方法可以显著提高图像检索的性能。      

Center symmetric local binary co-occurrence pattern for texture,face and bio-medical image retrieval

Content based image retrieval is a common problem for a large image database. Many methods have been proposed for image retrieval for some particular type of datasets. In the proposed work, a new image retrieval technique has been introduced. This technique is useful for different kind of dataset. In the proposed method, center symmetric local binary pattern has been extracted from the original image to obtain the local information. Co-occurrence of pixel pairs in local pattern map have been observed in different directions and distances using gray level co-occurrence matrix. Earlier methods have utilized histogram to extract the frequency information of local pattern map but co-occurrence of pixel pairs is more robust than frequency of patterns. The proposed method is tested on three different category of images, i.e., texture, face and medical image database and compared with typical state-of-the-art local patterns.     

基于任意BELL态的量子密钥分配

为了提高量子密钥分配的安全性和效率,利用量子纠缠交换的规律,提出了基于纠缠交换的量子密钥分配协议。通信双方通过简单的BELL测量建立起共享密钥,窃听者不可能窃取密钥而不被发现。该协议与其它分配协议的不同在于,可以实现对任意两个BELL态进行BELL测量达到量子密钥分配的目的。协议的实现只需要EPR粒子对,而不需要制备多粒子纠缠态。分析结果表明,此协议只用到两粒子的纠缠态,不需要进行幺正操作,它不仅能够保证密钥分配的安全性,而且简单高效。