Hai Yang Zhu^1,∗,†, Xiaobo Yin^1,∗,‡, Shunxiang Zhang^1,∗,§, Zhongliang Wei^1,¶, Guangli Zhu^1,II, Meng-Yen Hsieh^2,*,**

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 >

Pingshui Wang^1,*, Zecheng Wang¹, Tao Chen^1,2, 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 >