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基于同态加密的室内指纹定位隐私保护方案
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Abstract:
在室内定位服务中，如何保护用户及位置服务提供商的隐私安全和提高定位实时性一直是一个具有挑战性的问题。已有的方式大都采用欧氏距离结合半同态加密算法来完成，存在定位实时性不高、双方计算开销大等问题，基于此本文提出一种结合Kumar-Hassebrook距离、半同态加密算法及其安全点积性质的定位方案，在提高定位实时性的同时，能实现对用户位置信息和服务商指纹及位置数据隐私的保护。方案采取KH距离来匹配定位用户与指纹库中指纹数据的相似度，得到最近的K个最近邻参考点；在最近邻匹配中引入了半同态加密算法，保护用户和服务商的指纹数据隐私；同时，利用其安全点积性质实现了对服务商的指纹库坐标数据的隐私保护。为降低时间开销，引入了分簇聚类和模糊簇匹配，在提高定位实时性的同时可模糊服务器端对用户所在真实的簇的判断。从理论上对所提方案的安全性，时间开销及定位性能进行了分析，并在公共数据集中进行了性能评估。与同类加密算法比较，在不降低定位性能及安全性的前提下，该方案进一步地降低了时间开销。
How to protect the privacy security of users and location service providers and improve the real-time location performance has always been a challenging problem in indoor location services. Based on this, this paper proposes a solution that combines Kumar-Hassebrook distance, semi-homomorphic encryption algorithm and its security point product properties to protect the location and fingerprint data privacy of users and service providers and improve the real-time performance of positioning. The KH distance is used to match the similarity between the positioning user and the fingerprint data in the fingerprint database, and the nearest K nearest neighbor reference points are obtained. A semi-homomorphic encryption algorithm is introduced in Nearest Neighbor Matching to protect the privacy of fingerprint data of users and service providers. At the same time, the privacy protection of the fingerprint database coordinate data of the service provider is realized by using its secure dot product nature. In order to reduce the time overhead, clustering and fuzzy cluster matching are introduced, which can improve the real-time positioning and blur the judgment of the real cluster where the user is located on the server. Theoretically, the security, time overhead and positioning performance of the proposed scheme are analyzed, and the performance evaluation is carried out in the public dataset. Compared with similar encryption algorithms, the proposed scheme further reduces the time overhead without reducing the positioning performance and security.
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