PeCAN :یک معماری برای زمینه های تجارت الکترونیک آگاهی حریم خصوصی کاربران در وب معنایی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|3421||2006||26 صفحه PDF||سفارش دهید||13222 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Information Systems, Volume 31, Issues 4–5, June–July 2006, Pages 295–320
Supporting e-Commerce on the Semantic Web implies more sophisticated integration of Web services, agent interaction, domain ontologies, and data markup languages than is being done on today's Web. We explore user e-commerce, trust, and privacy scenarios and provide a vision for future e-commerce interactions with a more informed and in-control user. We present the Personal Context Agent Networking (PeCAN) knowledge architecture consisting of client-side and web-side architectural data components and services which inform the user of online privacy and trust within e-commerce tasks. A novel organization scheme and the composition of user contexts in this environment are proposed. Client-side ontologies and data structures for representing user contexts are introduced. For proof of concept, we describe a data belief ontology and illustrate PeCAN's compliance to the P3P privacy data schema. We use OWL as an implementation basis for maintaining privacy-aware e-commerce contextual knowledge for effective agent action in the PeCAN environment.
نتیجه گیری انگلیسی
We present an agent-based architecture, PeCAN which supports privacy-aware user contexts for electronic commerce on the Semantic Web. The architecture supports a novel definition of the user context as a collection of various social, organizational, technical, regulatory, data, stakeholder, and e-commerce transaction beliefs. We create a feasible design for organizing these privacy-aware user contexts. OWL ontologies are entries in various relevant beliefs repositories and are extracted and merged to form a user context. It is the knowledge derived from the merged graphs, i.e., the ontology-based context, that supports user rule formation and decision-making around privacy, and ultimately the management of private data, in electronic commerce situations. Complementary Web privacy ontologies form a web-side architectural component for privacy support and we outlined a sophisticated Web service to further illustrate the vision of the PeCAN system to empower the user with privacy-related information. Kobsa , in his recommendations for future directions in e-privacy, says “client side instead of server-side personalization would give users exclusive control of all purposely collected personal data as well as all processes that operate on these data.” Independently, our analyses of the privacy and data requirements for user e-commerce tasks have led to a client-side architecture consisting of a collection of collaborating agents, with distinct and separate tasks, that access a number of supporting repositories. The openness of the architecture also supports evolution of functionalities to support the various foreseen, and yet unforeseen, requirements stemming from new laws, regulations, and ethical standards and policies that are emerging. We are aware that there will be a potential efficiency penalty for organizing our client-side repositories in OWL format; we have traded this penalty off for gaining richness in semantic knowledge and the support of interoperability with allowed external entities. We have designed the PeCAN architecture and its privacy application with a future state of user awareness around privacy issues in mind. As such, it is difficult at this time to conduct proper usability studies that will fairly evaluate this system. As was the case for electronic commerce systems, new performance metrics need to be designed or borrowed from other disciplines and refined—not only based on response time, throughput, or web interactions per second but on more complex constructs such as effectiveness, ease of use, and perceived usefulness. The identification of these metrics is not a problem. The issue for a true evaluation is that governments and industry watchdogs still have much to do in raising citizen's awareness around privacy issues. We are concerned that any sample we pull today will be biased by lack of education about electronic privacy issues in the general user population. Thus, a careful design for evaluation study must be carried out for applications that are forward-looking—the subject of a future paper. In this paper, however, we make a qualitative case for why PeCAN agents are useful to the pragmatic majority of users. Privacy control should mean adding user control to data collection activities in terms of the user exercising choice to opt in/out, or provide data or not, having the rights to access and correct her personally identifiable information (PII) and to object to incorrect use, and place limits on who can access her PII, for what purposes, and know physical (where) and temporal (when) of storage of her PII. This is all well and good, but when users deal with many tens of businesses, government agencies, associations, communities over years—agents become useful and necessary to fill in forms for you while respecting current privacy preferences, dynamically change your contextual beliefs as your experiences grow, transparently renegotiate critical contracts around protection of personal data, provide you with summary reporting on dissemination of private information, or perform other management tasks for personal privacy. Other agent tasks, perhaps outside of electronic commerce contexts, that PeCAN can support in future include the automatic checking of the correctness of your personal data at potentially dozens of external sites. As it is now, we must arduously self-serve to look at data at each site. Stakeholders in privacy have declared themselves in many ways. Governments in conjunction with private sector around the world are working on initiatives to break the trust barriers to e-commerce adoption in their small and medium sized enterprises. A market for user-based privacy enhancing technologies and tools is emerging and in future we intend to support seamless integration of these tools in the PeCAN architecture. The target market for PeCAN is the growing group of users that want to have more hands-on, effective control over their online privacy while engaging in e-commerce.