Indoor localization is widely used as enabling technology for location-based services, such as advertising, indoor routing, and behavioral analysis. To keep these features available, service providers passively collect a large amount of data that may reveal strictly personal information about an individual. As an example, a timestamped mobility trace acquired in a mall may help the business owner to rearrange the user surroundings relying on a punctual analysis of the user behavior. In this paper we discuss some information processing techniques relying on probabilistic data structures designed to mitigate the user’s privacy leakage. The work is also accompanied by a case study. Our experiments were carried out using well-known networking equipment, Cisco Meraki, which is provided in combination with several primitives designed to passively infer and collect the user position in an indoor environment.

室内本地化被广泛用作基于位置的服务的使能技术，例如广告，室内路由和行为分析。为了保持这些功能的可用性，服务提供商会被动地收集大量数据，这些数据可能会严格泄露有关个人的个人信息。作为示例，在购物中心中获取的带有时间戳记的移动轨迹可以帮助企业主依靠对用户行为的守时分析来重新布置用户环境。在本文中，我们讨论了一些信息处理技术，这些信息处理技术依赖于概率数据结构，旨在减轻用户的隐私泄漏。这项工作还附带一个案例研究。我们的实验是使用著名的网络设备Cisco Meraki，