摘要
社交网络在提供位置交友等服务时,会对展示的用户距离文本进行混淆处理,以保护用户位置隐私。为了验证当前社交网络采用的位置混淆机制能否有效保护用户的精确位置不被泄露,提出了一种基于加权最小二乘的社交网络用户定位方法。该方法构造测试环境,对位置交友服务中混淆后的距离文本进行大量搜集和统计,结合真实距离数据识别报告距离对应的真实距离边界;基于对目标用户所处坐标系象限的判别,优化探针位置部署,并利用三边测量定位模型得到目标用户的多个初步位置估计;基于估计位置与探针的距离关系,分别确定目标用户相较各探针的最远距离和最近距离的权重,从而构造目标函数并基于加权最小二乘求目标函数的最优解,该最优解即目标用户的最终定位结果。该方法基于距离边界约束推断社交用户位置,避免了对位置服务的频繁查询,保证了对社交用户的定位效率。基于微信平台开展了社交用户定位实验,对500个微信用户的实际定位结果表明,该方法能够实现对微信“附近的人”用户的准确定位,与现有基于空间划分、基于启发式数论等典型定位方法相比,定位精度和效率均有更好的性能表现,平均定位误差降低了10%以上,定位过程中的位置服务访问次数减少了50%以上。
When providing location-based dating and other location-based services, social networks will confuse the displayed user distance text to protect the user’s location privacy. In order to verify whether the current location confusion mechanism adopted by social networks can effectively protect user’s accurate location, a social network user geolocating method based on weighted least squares was proposed. The method constructed real-world tests to collect a large number of confused distance texts in location dating service, and identified the real distance boundary of reported distance combined with real distance data. Then, based on the discrimination of the quadrant of the coordinate system where the target user was located, the position of probes was optimally deployed, and multiple preliminary position estimations of the target user were obtained by using the trilateration model. The weights of the longest and the shortest distance of the target user were determined by the estimated position, and the related objective function was constructed. The optimal solution of the objective function was calculated based on the weighted least squares, which was the final geolocating result of the target user. The proposed method inferred the location of social network user based on the distance boundary constraint, which avoided the frequent query of location services and ensured the geolocating efficiency. The actual geolocating results based on 500 We Chat users showed that, the proposed method can accurately geolocate the “eople nearby” users of We Chat. Compared with the existing typical geolocating methods based on space partition and heuristic number theory, the proposed method have better performance in geolocating accuracy and efficiency. The average geolocating error is reduced by more than 10%, and the number of location service quarries in the geolocating process is reduced by more than 50%.
作者
时文旗
罗向阳
郭家山
SHI Wenqi;LUO Xiangyang;GUO Jiashan(Key Laboratory of Cyberspace Situation Awareness of Henan Province,Zhengzhou 450001,China;State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China;School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450001,China)
出处
《网络与信息安全学报》
2022年第3期41-52,共12页
Chinese Journal of Network and Information Security
基金
国家自然科学基金(1804263,1736214,62172435,62002386)
中原科技创新领军人才计划(214200510019)。
关键词
社交网络
位置隐私
用户定位
加权最小二乘
social network
location privacy
user positioning
weighted least squares
