群智感知中基于k-匿名的位置及数据隐私保护方法研究

Research on k-anonymity-based privacy protection in crowd sensing

随着无线智能移动终端设备的爆炸式普及,群智感知应用越来越广泛,是当前的研究热点。然而,用户的隐私泄露问题成为阻碍用户积极参与性的关键因素。基于此,提出一种基于k-匿名的位置及数据隐私保护方法(KPPCS)。首先,采用基于多方安全合作的方法来构造一个包含n个互相没有任何链接的用户的等价类,以保证参与用户的位置隐私。同时,采用数据迭代的方法,使数据在等价类中进行迭代,直到最后一个用户将所有数据上传到服务器,从而确保参与用户数据的隐私性。实验结果及理论分析表明,KPPCS不仅能够保证用户的位置信息及数据所有权信息的安全,且降低了服务器以及用户设备的开销。

With the explosive popularity of wireless intelligent mobile terminal devices,the application of crowd sensing is more and more widely used,which is a hot research topic.However,the user's privacy breach has become a key factor hindering the active participation of users.Based on this,a k-anonymous location and data privacy protection method(KPPCS)was proposed.First,a multi-party security cooperation based approach was used to construct an equivalence class containing n users who do not have any links to each other to ensure the privacy of the participating users.Then,using data iteration,the data is iterated in the equivalence class until the last user uploads all the data to the server,ensuring the data privacy of the participating users.The experimental results and theoretical analysis show that KPPCS can not only ensure the security of the user's location information and data ownership information,but also reduce the cost of the server and user equipment.