A profile anonymization model for location-based services

Location-based services LBS aim at delivering point of need information. Personalization and customization of such services, based on the profiles of mobile users, would significantly increase the value of such services. Since profiles may include sensitive information of mobile users and moreover can help identify a person, such customization is allowable only when the security and privacy policies dictated by them are respected. While LBS providers are presumed to be untrusted entities, the location services that capture and maintain mobile users' location to enable communication are considered trusted, and therefore can capture and manage the profile information. The question then is, how to enable the use of location based services while protecting privacy?In this paper, we address the problem of privacy preservation via anonymization. Prior research in this area attempts to ensure k-anonymity by generalizing the location. However, a person may still be identified based on his/her profile if the profiles of all k people in the generalized region are not the same. We extend the notion of k-anonymity by proposing a profile based k-anonymization model that guarantees anonymity even when profiles of mobile users are revealed to untrusted entities. Specifically, our anonymization methods generalize both location and profiles to the extent specified by the user. We propose a novel unified index structure, called the PTPR-tree to enhance the performance during anonymization. PTPR-tree is an extension of the TPR-tree [in: SIGMOD'00: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, ACM, 2000, pp. 331--342] which organizes both the locations of mobile users as well as their profiles using a single index, and therefore can efficiently find candidate users for the proposed profile based anonymization models.

基于位置的服务（LBS）旨在提供所需地点的信息。基于移动用户的个人资料对这类服务进行个性化和定制，将显著提高此类服务的价值。由于个人资料可能包含移动用户的敏感信息，而且有助于识别一个人，只有当尊重由这些资料所规定的安全和隐私政策时，这种定制才是允许的。虽然LBS提供商被假定为不可信的实体，但获取和维护移动用户位置以实现通信的位置服务被认为是可信的，因此可以获取和管理个人资料信息。那么问题是，如何在保护隐私的同时启用基于位置的服务呢？在本文中，我们通过匿名化来解决隐私保护的问题。该领域先前的研究试图通过对位置进行泛化来确保k - 匿名性。然而，如果在泛化区域内所有k个人的个人资料不完全相同，一个人仍可能基于他/她的个人资料被识别出来。我们通过提出一种基于个人资料的k - 匿名化模型扩展了k - 匿名性的概念，该模型即使在移动用户的个人资料被泄露给不可信实体时也能保证匿名性。具体来说，我们的匿名化方法将位置和个人资料都按照用户指定的程度进行泛化。我们提出一种新颖的统一索引结构，称为PTPR - 树，以提高匿名化过程中的性能。PTPR - 树是TPR - 树[见：SIGMOD'00：2000年美国计算机协会数据管理国际会议论文集，美国纽约，美国计算机协会，2000年，第331 - 342页]的扩展，它使用单一索引来组织移动用户的位置以及他们的个人资料，因此可以有效地为所提出的基于个人资料的匿名化模型找到候选用户。