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This paper proposes a practical payment protocol with scalable anonymity for Internet purchases, and analyzes its role- based access control (RBAC). The protocol uses electronic cash for payment transactions. It is an offline payment scheme that can prevent a consumer from spending a coin more than once. Consumers can improve anonymity if they are worried about disclosure of their identities to banks. An agent provides high anonymity through the issue of a certification. The agent certifies reencrypted data after verifying the validity of the content from consumers, but with no private information of the consumers required. With this new method, each consumer can get the required anonymity level, depending on the available time, computation, and cost. We use RBAC to manage the new payment scheme and improve its integrity. With ABAC, each user may be assigned one or more roles, and each role can be assigned one or more privileges that are permitted to users in that role. To reduce conflicts of different roles and decrease complexities of administration, duty separation constraints, role hierarchies, and scenarios of end-users are analyzed. [ABSTRACT FROM AUTHOR]

As dynamic content becomes increasingly dominant, it becomes an important research topic as how the edge resources such as client-side proxies, which are otherwise underutilized for such content, can be put into use. However, it is unclear what will be the best strategy, and the design/deployment trade offs lie therein, in this paper, using one representative e-commerce benchmark, we report our experience of an extensive investigation of different off loading and caching options. Our results point out that, while great benefits can be reached in general, advanced offloading strategies can be overly complex and even counterproductive. In contrast, simple augmentation at proxies to enable fragment caching and page composition achieves most of the benefit without compromising important considerations such as security. We also present Proxy+ architecture which supports such capabilities for existing Web applications with minimal reengineering effort. [ABSTRACT FROM AUTHOR]

With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: “reviewer-related” features, “review subjectivity” features, and “review readability” features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact. [ABSTRACT FROM AUTHOR]