Purpose： In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for cu...Purpose： In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements. Design/methodology/approach： We worked with two different data sets to examine whether Twitter users＇ gender, inferred from the first name of the account and the profile description, correlates with the privacy setting of the account. We also used a set of features including the inferred gender of Twitter users to develop classifiers that predict user privacy settings. Findings： We found that the inferred gender of Twitter users correlates with the account＇s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user＇s privacy preference. Research limitations： It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A maj or limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, that is, accounts that had names non-existent in the name dictionary, are social bots. It will be interesting to explore the privacy setting of social bots in the Twitter space. Practical implications： Companies are investing large amounts of money in business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users＇ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users＇ provided first names and their profile descriptions, can be used to infer the users＇ privacy preference.Originality/value： This study explored a new way of inferring Twitter user＇s gender, that is, to recognize the user＇s gender based on the provided first name and the user＇s profile description. The potential of this information for predicting the user＇s privacy preference is explored.展开更多
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 ...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 could improve the security of the mobile social network while keeping high execution efficiency.展开更多
Location privacy receives considerable attentions in emerging location based services.Most current practices however either ignore users＇ preferences or incompletely fulfill privacy preferences.In this paper,we propo...Location privacy receives considerable attentions in emerging location based services.Most current practices however either ignore users＇ preferences or incompletely fulfill privacy preferences.In this paper,we propose a privacy protection solution to allow users＇ preferences in the fundamental query of k nearest neighbors （kNN）.Particularly,users are permitted to choose privacy preferences by specifying minimum inferred region.Via Hilbert curve based transformation,the additional workload from users＇ preferences is alleviated.Furthermore,this transformation reduces time-expensive region queries in 2-D space to range the ones in 1-D space.Therefore,the time efficiency,as well as communication efficiency,is greatly improved due to clustering properties of Hilbert curve.Further,details of choosing anchor points are theoretically elaborated.The empirical studies demonstrate that our implementation delivers both flexibility for users＇ preferences and scalability for time and communication costs.展开更多
文摘Purpose： In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements. Design/methodology/approach： We worked with two different data sets to examine whether Twitter users＇ gender, inferred from the first name of the account and the profile description, correlates with the privacy setting of the account. We also used a set of features including the inferred gender of Twitter users to develop classifiers that predict user privacy settings. Findings： We found that the inferred gender of Twitter users correlates with the account＇s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user＇s privacy preference. Research limitations： It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A maj or limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, that is, accounts that had names non-existent in the name dictionary, are social bots. It will be interesting to explore the privacy setting of social bots in the Twitter space. Practical implications： Companies are investing large amounts of money in business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users＇ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users＇ provided first names and their profile descriptions, can be used to infer the users＇ privacy preference.Originality/value： This study explored a new way of inferring Twitter user＇s gender, that is, to recognize the user＇s gender based on the provided first name and the user＇s profile description. The potential of this information for predicting the user＇s privacy preference is explored.
基金We thank the anonymous reviewers and editors for their very constructive comments.This work was supported by the National Social Science Foundation Project of China under Grant 16BTQ085.
文摘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 could improve the security of the mobile social network while keeping high execution efficiency.
基金Supported by the National Natural Science Foundation of China under Grant Nos. 61003057 and 60973023
文摘Location privacy receives considerable attentions in emerging location based services.Most current practices however either ignore users＇ preferences or incompletely fulfill privacy preferences.In this paper,we propose a privacy protection solution to allow users＇ preferences in the fundamental query of k nearest neighbors （kNN）.Particularly,users are permitted to choose privacy preferences by specifying minimum inferred region.Via Hilbert curve based transformation,the additional workload from users＇ preferences is alleviated.Furthermore,this transformation reduces time-expensive region queries in 2-D space to range the ones in 1-D space.Therefore,the time efficiency,as well as communication efficiency,is greatly improved due to clustering properties of Hilbert curve.Further,details of choosing anchor points are theoretically elaborated.The empirical studies demonstrate that our implementation delivers both flexibility for users＇ preferences and scalability for time and communication costs.
