改进的数字版权保护模型

为有效解决目前数字版权保护模型中存在的对称性和不具备可撤销性等问题,提出了一种改进的基于叛逆者追踪方案的数字版权保护模型。该改进模型应用了不经意多项式估值(OPE)协议的特点,用户在注册阶段,商家和用户同时执行OPE协议,真正实现了两者之间的非对称性。针对买家付款后却可能收不到产品的问题,新模型还引进了既可以保护商家利益又可以保护用户利益的可信中心(TC),使其更具有实用性。改进模型还增加了软件服务撤销功能,该算法进一步完善了版权保护。通过具体实例表明了该模型的可行性和有效性。     

群签名在DRM系统隐私保护中的应用研究

现在的数字版权管理（DRM）系统注重的只是防止数字内容被非法复制以及保障内容服务商的利益,并没有充分考虑到用户的购买匿名性。具体研究分析了现有的群签名方案,并结合群签名技术,提出一种改进的具有匿名性的DRM系统模型,从而使得合法用户既具有购买匿名性,同时又可以揭露非法用户的真实身份。     

基于水印和密码的数字版权管理模型的研究

在分析了数字版权研究现状和功能框架的基础上,设计了基于多重数字水印和密码的版权管理模型;该模型采用数字内容和许可证各自单独封装、独立分发的方式,使得只有购买了许可证的特定用户才能使用数字内容,实现了水印作品的许可交易;另外,内容服务器采用的对数字作品加载两次水印,对数字内容部分签名加密,未加密部分签名的方案,在有效保证数字内容安全传输的基础上又减少了计算量和存储量;分析表明该模型有显著的优点和很好的安全性。     

基于机器指纹的移动数字版权管理模型的研究

本文提出了一种基于机器指纹的移动数字版权管理模型，此模型采用一种新的许可证密钥产生、分配和认证方法，使内容许可证跟用户的机器指纹和用户的个人信息绑定。数字内容采取自主设计的安全性达到一定要求的对称密钥算法加密。用户只能在本地移动设备上按照内容许可证的要求进行解密度可控使用数字内容，从而在整个数字内容生命周期实行版权保护。     

面向机顶盒的数字版权管理模型

提出一种面向机顶盒的数字版权管理安全模型。将数字内容与版权相分离,通过内容加密密钥保护数字内容,利用与客户设备信息相关的设备密钥保护每一份许可证,在用户与服务器之间实现双向认证。实验结果表明,该模型利用加解密技术、数字签名技术和授权管理等技术,能实现对高清内容、MTV、TV媒体杂志等数字内容的控制和管理。     

一种基于虹膜识别技术的数字版权管理系统

数字版权管（DRM）系统是用于数字交易的有效保护方案。基于高安全性的虹膜生物识别方法,本文提出了一种新的DRM模型。该模型能够稳定而准确鉴别用户身份和权限,完成对数字内容使用的权限管理。为了克服现有DRM系统中数字内容分发存在的安全漏洞,在对用户的身份识别过程中,采用自动虹膜识别方法而不是传统的密码口令。为了保证生物数据的安全性,此模式采用基于PKI的安全交互协议。分析和研究表明此模型有较高的安全性、可靠性,适用于数字内容的安全分发。     

一种新的DRM权限跟踪模型*

提出的基于身份的DRM权限跟踪模型,即授权用户可以在不同的设备上播放数字内容,提高了互操作性。内容服务器对数字内容部分加密,未加密部分嵌入数字水印。部分加密在保证安全的基础上,减小了计算复杂度。数字水印有双重功能：一是标志该内容受保护;二是颁发许可证依据,许可证服务器根据检测到的水印信息向用户颁发相应的许可证。数字水印是对用户使用权限的定义,数字水印的加载,实现了对数字内容整个生命周期的权限跟踪。分析表明该模型具有很好的安全性和有效性。     

基于数字水印和加密技术的DRM系统模型设计

本文分析了传统的数字版权管理系统模型,针对其在发生版权纠纷时无法提供有效版权凭证缺陷,构建了一种利用加密技术和数字水印技术相结合的DRM（数字版权管理）系统模型。运用数字水印技术,在数字内容中嵌入版权水印和用户水印,为数字作品在发生版权纠纷时,提供版权认证和用户认证.     

基于动态规划算法的自律微电网优化方法研究

本文中建立了一种基于动态规划算法,主要考虑铅酸蓄电池运行成本及发电单元运行维护成本的自律徽电网用户在并网模式下经济利益最大化的优化模型,并在Matlab环境下根据优化模型建立了仿真算例。仿真结果验证了该方法对于自律微电网用户的经济化运行具有可行性和有效性。     

区块链环境下支持隐私保护的数字权限管理

针对数字权限保护中对数字内容安全和用户隐私保护的需求,提出了一种支持区块链环境下隐私保护的数字权限保护方案,设计了区块链环境下数字内容权限全生命周期保护和用户隐私保护的框架,主要包括内容加密、许可授权和内容解密3个协议。利用Diffie-Hellman密钥交换和加法同态加密算法,实现了内容加密密钥的保护和分发,同时保证了内容加密密钥的安全性和用户的隐私性,防止区块链中的其他节点收集用户的敏感信息,如用户的使用习惯。与传统的数字权限保护方案相比,该方案基于区块链具有信息公开透明、信息不可篡改等特点,并且保护了内容安全和用户的隐私,具有较好的实用性。安全性分析表明,该方案在区块链环境下是安全的;仿真实验结果表明,该方案能够以较低的开销实现用户的隐私保护。     

在线技术社区的用户技能与兴趣发现

在线技术社区是技术爱好者或者从业者进行技术交流、咨询和分享的重要平台。社区运营者如果能够准确掌握每个用户的技能和兴趣,对用户进行画像,将有助于为用户提供精准的推荐和个性化服务,从而增加用户的黏性和社区的活跃度。考虑到社区用户既是内容的生产者(作者)又是内容的消费者(读者),生产者体现用户技能,消费者体现用户兴趣,从而提出了一种作者—读者—话题(author-reader-topic,ART)模型,同时对用户的技能和兴趣进行建模。该模型可以将文档的作者和读者关联起来,因而能够提升话题的聚集效果,产生更准确的作者话题分布和读者话题分布。该文基于CSDN技术社区的真实数据集进行了实验对比和分析,实验结果表明,该文提出的ART模型能够有效地发现用户的技能和兴趣,明显优于现有的各种话题模型。     

Enabling the new economic actor: data protection,the digital economy,and the Databox

This paper offers a sociological perspective on data protection regulation and its relevance to design. From this perspective, proposed regulation in Europe and the USA seeks to create a new economic actor—the consumer as personal data trader—through new legal frameworks that shift the locus of agency and control in data processing towards the individual consumer or “data subject”. The sociological perspective on proposed data regulation recognises the reflexive relationship between law and the social order, and the commensurate needs to balance the demand for compliance with the design of computational tools that enable this new economic actor. We present the Databox model as a means of providing data protection and allowing the individual to exploit personal data to become an active player in the emerging data economy.     

一种基于用户行为的兴趣度模型

个性化推荐技术在电子商务系统中得到了广泛应用。针对现有的用户模型不能根据用户自身兴趣实现推荐的问题,提出了一种基于用户行为的兴趣度模型,分析用户的行为模式,结合用户的浏览内容,发现用户兴趣。在此基础上采用期望最大化算法实现用户聚类,将用户划分到对应的簇,创建用户的兴趣度模型,从而向用户进行个性化推荐。实验对比结果表明,该模型能更好地发现用户当前的购买兴趣,从而进一步提高个性化推荐精度和用户满意度。     

新型消费文化趋势下的网络广告策略

网络技术发展和应用的日益广泛,促进了传统消费文化的快速变革,形成一种网络时代的新型消费文化。在这种新型消费文化的影响下,网络广告作为现代企业展开网络营销活动最重要的手段由此应运而生。网络广告设计理念也将具有全新的内涵,广告策略也将一改传统模式而独具特色,趋向于凝结市场、消费场所、审美情趣及生活方式等众多因素的整合,使得网络设计的视觉传达、技术应用、表现形式、运作模式等呈现出全新的面貌。     

电子商务与电子商务经济：概念与框架

作为一个新的概念,电子商务经济来自电子商务,但是却比其内涵更为丰富。为此,我们一方面要从信息化、参与主体、电子商务建设服务的供给与需求、产业属性等角度全面认识电子商务经济的基本属性,另一方面还要从物联网、云计算、大数据以及移动智能终端为主要代表的新一代信息技术去把握电子商务经济发展的最新特征。电子商务经济是以电子商务平台为核心,以电子商务应用需求、电子商务服务业为两翼,以新一代信息技术应用为支撑,包含众多信息消费内容的新型经济生态系统。电子商务经济是我国电子商务发展到一个相对成熟阶段的表现,也是新一代信息技术在我国经济信息化建设中得到深入应用的结果。电子商务经济正日益成为促进国民经济和社会发展信息化建设的主要力量。     

A personalized recommendation method under the cloud platform based on users’ long-term preferences and instant interests

Rich consumer online text data are embedded in the cloud platform. Using new technologies has become a central issue for acquiring consumer preference, analyzing consumer demand, and performing personalized recommendation services. In order to recommend the cloud platform services efficiently and accurately, this paper proposes a personalized recommendation model referred to as Residual bi-directional Recurrent Neural Network with Dual Attentive mechanism (BiRDA) for the service recommend to cloud platforms, by combining users’ long-term preferences with instant interest. The proposed recommender prototype is summarized as follows. (1) Analyzing the relationship between long-term preferences and instant interests based on co-opetition theory. (2) Extracting users’ online text data from the cloud platform. (3) Deriving the product attribute words of user preference using an analysis of online text data. (4) Product attribute words are transformed into the form of word vectors. (5) The word vector is input into the Residual bi-directional Recurrent Neural Network (Res-BiRNN) to make the prediction. On the one hand, the long-term preference is expressed by the user's field of expertise (i.e., answer content). On the other hand, the even interest is expressed by the user's changing interest (i.e., question data). (6) Assigning different weights to long-term preferences and instant interest using the dual attention mechanism to output predictions. (7) Generating recommendation lists for users based on the predicted values. Accordingly, BiRDA is compared with five state-of-the-art recommendation methods (i.e., DREAM, BINN, SHAN, Caser, and DeepMove), as well as six variants of the BiRDA model, Using users’ Q&A datasets from NiorcngeCDS cloud platform, XMAKE cloud platform, and Asksubarme cloud platform as examples. The experiments show that the proposed method is more efficient and accurate than the other models. Therefore, the study offers some important insights into allowing a large number of resources under the cloud platform to be fully utilized and provides a novel idea for the construction of the cloud platform front-end.     

Interest based selection of user generated content for rich communication services

The last few years, we have witnessed an exponential growth in available content, much of which is user generated (e.g. pictures, videos, blogs, reviews, etc.). The downside of this overwhelming amount of content is that it becomes increasingly difficult for users to identify the content they really need, resulting into considerable research efforts concerning personalized search and content retrieval.On the other hand, this enormous amount of content raises new possibilities: existing services can be enriched using this content, provided that the content items used match the user's personal interests. Ideally, these interests should be obtained in an automatic, transparent way for an optimal user experience.In this paper two models representing user profiles are presented, both based on keywords and with the goal to enrich real-time communication services. The first model consists of a light-weight keyword tree which is very fast, while the second approach is based on a keyword ontology containing extra temporal relationships to capture more details of the user's behavior, however exhibiting lower performance. The profile models are supplemented with a set of algorithms, allowing to learn user interests and retrieving content from personal content repositories.In order to evaluate the performance, an enhanced instant messaging communication service was designed. Through simulations the two models are assessed in terms of real-time behavior and extensibility. User evaluations allow to estimate the added value of the approach taken. The experiments conducted indicate that the algorithms succeed in retrieving content matching the user's interests and both models exhibit a linear scaling behavior. The algorithms perform clearly better in finding content matching several user interests when benefiting from the extra temporal information in the ontology based model.     

An effective content-based event recommendation model

Event-based social networks (EBSNs) facilitate people to interact with each other by sharing similar interests in online groups or taking part in offline events together. Event recommendation in EBSNs has been studied by many researchers. However, the problem of recommending the event to the top N active-friends of the key user has rarely been studied in EBSNs. In this paper, we propose a new method to solve this problem. In this method, we first construct an association matrix from the content of events and user features. Then, we define a new content-based event recommendation model, which combines the matrix, spatio-temporal relations and user interests to recommend an event to the active-friends of a key user. A series of experiments were conducted on real datasets collected from Meetup, and the comparison results have demonstrated the effectiveness of the new model.

     

Understanding consumer intention in online shopping: a respecification and validation of the DeLone and McLean model

The increasing popularity of online shopping has led to the emergence of new economic activities. To succeed in the highly competitive e-commerce environment, it is vital to understand consumer intention. Understanding what motivates consumer intention is critical because such intention is key to survival in this fast-paced and hypercompetitive environment. Where prior research has attempted at most a limited adaptation of the information system success model, we propose a comprehensive, empirical model that separates the ‘use’ construct into ‘intention to use’ and ‘actual use’. This makes it possible to test the importance of user intentions in determining their online shopping behaviour. Our results suggest that the consumer's intention to use is quite important, and accurately predicts the usage behaviour of consumers. In contrast, consumer satisfaction has a significant impact on intention to use but no direct causal relation with actual use.