خوشه بندی رفتار کاربر با برچسب گذاری خودکار
ترجمه نشده

خوشه بندی رفتار کاربر با برچسب گذاری خودکار

عنوان فارسی مقاله: طرح خوشه بندی رفتار کاربر با برچسب گذاری خودکار بر روی داده های رمزگذاری شده
عنوان انگلیسی مقاله: User Behavior Clustering Scheme With Automatic Tagging Over Encrypted Data
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات، امنیت اطلاعات
کلمات کلیدی فارسی: خوشه بندی رفتار کاربر، داده های رمزگذاری شده، خوشه بندی با برچسب گذاری
کلمات کلیدی انگلیسی: User behavior clustering, encrypted data, clustering with tagging
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2956019
دانشگاه: China NARI Group Corporation (State Grid Electronic Power Research Institute), Nanjing 210003, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14064
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Preliminaries


III. Design


IV. Experiment and Analysis


V. Conclusion


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


User behavior clustering analysis has a wide range of applications in business intelligence, information retrieval, and image pattern recognition and fault diagnosis. Most of existing methods of user behavior have some problems such as weak generality and the lack of tags of clustering. With the increasing awareness of privacy protection, user behavior analysis also needs to support for ciphertext to protect user data. Based on clustering algorithm, homomorphic encryption technology and information security, in this paper, we propose a user behavior clustering scheme that supports automatic tags on ciphertext. Firstly, design a security protocol corresponding to the basic operations such as addition, multiplication and comparison and apply to the scheme. Then, the relevant features of the user behavior are merged with the clustering process, the latent factor model, and matrix decomposition. We have implemented our method and evaluated its performance using K-means and K-means++ clustering. The results show that the scheme can auto tags over encrypted data, and the tag also meets the actual situation, which proves the validity and generality of the scheme.


Introduction


With the increasing maturity of mobile Internet technology, people use various mobile devices and wireless communication networks to browse the web, read news and carry out social activities at any time and any place, and information exchange is more and more convenient. Massive data is constantly generated in various fields, which makes the Internet data and resources show massive characteristics. How to get useful information and knowledge from redundant data to help us make more objective and effective decisions has become an important problem. User behavior analysis can solve the above problems, which refers to the statistics and analysis of user interest. Clustering algorithm is a common means to achieve it, which is widely used in data statistical analysis fields such as business intelligence, information retrieval, image pattern recognition and fault diagnosis [1]. At present, most clustering algorithms still exit two problems: the number of clustering and the tags after clustering is unknown. Without iterating through the data in each group, the category represented by the group cannot be known. After clustering user behavior data, there is no suitable method to mark each group directly. For example, shopping websites usually record members to buy the product information or comments, and also has a product category. They wants to get each group of tags after clustering to combine them, so they can obtain information about what kind of products the users in the group like. Company can offer different marketing plans to different groups. While user behavior clustering is widely applied, it also causes serious privacy disclosure, which will bring harm to the data owner [2], [3]. For example, when using clustering for stock analysis, if the behavior information of individual stock is leaked in the process of clustering, it will bring chaos to the stock market. Criminals steal user behavior data, which often reflects the user’s interests and hobbies, criminals for this fraud.

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