ارزیابی اعتماد بر اساس نظریه شواهد در شبکه های اجتماعی آنلاین
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ارزیابی اعتماد بر اساس نظریه شواهد در شبکه های اجتماعی آنلاین

عنوان فارسی مقاله: ارزیابی اعتماد بر اساس نظریه شواهد در شبکه های اجتماعی آنلاین
عنوان انگلیسی مقاله: Trust evaluation based on evidence theory in online social networks
مجله/کنفرانس: مجله بین المللی شبکه های حسگر توزیع شده - International Journal Of Distributed Sensor Networks
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده
کلمات کلیدی فارسی: ارزیابی اعتماد، نظریه شواهد، شبکه های اجتماعی آنلاین، پیش بینی جریان اطلاعات، تصمیم گیری
کلمات کلیدی انگلیسی: Trust evaluation، evidence theory، online social networks، information flow prediction، decision making
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: DOAJ - Master Journal List - JCR - Scopus
شناسه دیجیتال (DOI): https://doi.org/10.1177/1550147718794629
دانشگاه: Zhengzhou University - Zhengzhou - China
ناشر: سیج - Sage
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 1/460 در سال 2017
شاخص H_index: 31 در سال 2019
شاخص SJR: 0/255 در سال 2017
شناسه ISSN: 1550-1477
شاخص Quartile (چارک): Q2 در سال 2017
فرمت مقاله انگلیسی: Pdf
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E10824
فهرست انگلیسی مطالب

Abstract


Introduction


Related works


Preliminary


Trust evaluation based on the combination of evidence


Experiment and analysis


Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


Trust is an important criterion for access control in the field of online social networks privacy preservation. In the present methods, the subjectivity and individualization of the trust is ignored and a fixed model is built for all the users. In fact, different users probably take different trust features into their considerations when making trust decisions. Besides, in the present schemes, only users’ static features are mapped into trust values, without the risk of privacy leakage. In this article, the features that each user cares about when making trust decisions are mined by machine learning to be User-Will. The privacy leakage risk of the evaluated user is estimated through information flow predicting. Then the User-Will and the privacy leakage risk are all mapped into trust evidence to be combined by an improved evidence combination rule of the evidence theory. In the end, several typical methods and the proposed scheme are implemented to compare the performance on dataset Epinions. Our scheme is verified to be more advanced than the others by comparing the F-Score and the Mean Error of the trust evaluation results.


Introduction


Online social networks (OSNs) are platforms or systems that people can interact with others by sharing or posting blogs online.1 Social networking is very common, such as Facebook, Tweeter, Weibo and CyVOD.2 These platforms provide a free space for everyone to unleash their mind and thoughts. However, it makes information leakage possible.3 The spammers spread malicious links and annoying messages to OSN users without target, and privacy information is unsafe for the cheating actions4 and blackmails.5 To prevent the malicious activities, many schemes such as Access Control6 and digital rights protection7–9 are proposed. In these schemes, trust degree is usually viewed as the main criterion for security policies to make the privacy management more feasible and effective. As it is important to privacy preservation in OSNs, trust evaluation has become a research focus in recent years.10–13 Researchers try to find the relationship between user features and trust decision. It is no doubt that trust decision is not only affected by objective features of each user but also affected by the subjective options of the user. For example, some people think the one who has a lot of fans in the OSNs is trustworthy, while others would rather choose the people who have higher credit or reputation. So, just a single model without individualization is insufficient to evaluate trust degree between users in OSNs.

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