With the proliferation of wireless communication and mobile devices, various location-based services are emerging. For the growth of the location-based services, more accurate and various types of personal location data are required. However, concerns about privacy violations are a significant obstacle to obtain personal location data. In this paper, we propose a local differential privacy scheme in an environment where there is no trusted third party to implement privacy protection techniques and incentive mechanisms to motivate users to provide more accurate location data. The proposed local differential privacy scheme allows a user to set a personalized safe region that he/she can disclose and then perturb the user’s location within the safe region. It is the way to satisfy the user’s various privacy requirements and improve data utility. The proposed incentive mechanism has two models, and both models pay the incentive differently according to the user’s safe region size to motivate to set a more precise safe region. We verify the proposed local differential privacy algorithm and incentive mechanism can satisfy the privacy protection level while achieving the desirable utility through the experiment.

中文翻译：

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本地环境中个性化差分私人位置数据的自适应网格和激励机制

随着无线通信和移动设备的激增，各种基于位置的服务正在兴起。为了基于位置的服务的增长，需要更准确和各种类型的个人位置数据。但是，对侵犯隐私的担忧是获取个人位置数据的重要障碍。在本文中，我们提出了一种在没有受信任的第三方来实施隐私保护技术和激励机制来激励用户提供更准确的位置数据的环境中的本地差异隐私方案。提出的本地差异隐私方案允许用户设置他/她可以公开的个性化安全区域，然后在安全区域内干扰用户的位置。这是满足用户各种隐私要求并提高数据实用性的方法。所提出的激励机制具有两个模型，并且这两个模型根据用户的安全区域大小来不同地支付激励，以激励设置更精确的安全区域。我们通过实验验证了所提出的局部差分隐私算法和激励机制能够满足隐私保护水平，同时达到期望的效用。