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  • Open Access

    ARTICLE

    A Discovery Method for New Words From Mobile Product Comments

    Hai Yang Zhu1,∗,†, Xiaobo Yin1,∗,‡, Shunxiang Zhang1,∗,§, Zhongliang Wei1,¶, Guangli Zhu1,II, Meng-Yen Hsieh2,*,**

    Computer Systems Science and Engineering, Vol.35, No.6, pp. 399-410, 2020, DOI:10.32604/csse.2020.35.399

    Abstract A large number of new words in product reviews generated by mobile terminals are valuable indicators of the privacy preferences of customers. By clustering these privacy preferences, sufficient information can be collected to characterize users and provide a data basis for the research issues of privacy protection. The widespread use of mobile clients shortens the string length of the comment corpus generated by product reviews, resulting in a high repetition rate. Therefore, the effective and accurate recognition of new words is a problem that requires an urgent solution. Hence, in this paper, we propose a… More >

  • Open Access

    ARTICLE

    Personalized Privacy Protecting Model in Mobile Social Network

    Pingshui Wang1,*, Zecheng Wang1, Tao Chen1,2, Qinjuan Ma1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 533-546, 2019, DOI:10.32604/cmc.2019.05570

    Abstract With the rapid development of the new generation of information technology, the analysis of mobile social network big data is getting deeper and deeper. At the same time, the risk of privacy disclosure in social network is also very obvious. In this paper, we summarize the main access control model in mobile social network, analyze their contribution and point out their disadvantages. On this basis, a practical privacy policy is defined through authorization model supporting personalized privacy preferences. Experiments have been conducted on synthetic data sets. The result shows that the proposed privacy protecting model More >

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