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Apart from node information, researchers have found that structure information can also leak data providers' privacy, especially in weighted social networks. However, most of the existing private\u2010preserving schemes focus on a single aspect. The comprehensive consideration introduces two challenges. On one hand, the different anonymity demands of node and structure information lead to the collision of different design criteria, which is called as consistency matching problem. On the other hand, the simple combination of existing schemes may introduce large amounts of unnecessary changes, which makes the published information meaningless. Thus, we must find the balance between anonymity demands and changes, which is called as optimization trade\u2010off problem. In this paper, we propose a \u03b3<\/jats:italic>\u2010Strawman privacy\u2010preserving scheme in weighted social networks to solve these challenges. To address consistency matching problem, we propose a greedy algorithm based on a user trade\u2010off metric. For optimization tradeoff problem, a closeness edge\u2010editing technology is considered, which can change the private information slightly. Finally, we evaluate our scheme on real\u2010world datasets, the experimental results show that the \u03b3<\/jats:italic>\u2010Strawman scheme is efficient. Copyright \u00a9 2017 John Wiley & Sons, Ltd.<\/jats:p>","DOI":"10.1002\/sec.1722","type":"journal-article","created":{"date-parts":[[2017,1,19]],"date-time":"2017-01-19T07:03:49Z","timestamp":1484809429000},"page":"5625-5638","source":"Crossref","is-referenced-by-count":1,"title":["A \u03b3<\/i>\u2010Strawman privacy\u2010preserving scheme in weighted social networks"],"prefix":"10.1002","volume":"9","author":[{"given":"Jing","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Software Engineering, School of Computer Wuhan University Wuhan China"},{"name":"Collaborative Innovation Center of Geospatial Technology Wuhan University Wuhan China"}]},{"given":"Bowen","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Engineering, School of Computer Wuhan University Wuhan China"}]},{"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China"}]},{"given":"Quan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Math and Computer Science University of Texas\u2010Permian Basin Odessa TX USA"}]},{"given":"Ruiying","family":"Du","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Engineering, School of Computer Wuhan University Wuhan China"},{"name":"Collaborative Innovation Center of Geospatial Technology Wuhan University Wuhan China"}]}],"member":"311","published-online":{"date-parts":[[2017,1,19]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"ZhangP ChenW SunX WangY ZhangJ.Minimizing seed set selection with probabilistic coverage guarantee in a social network.Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD) New York NY USA 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