多级敏感区域室内定位中的隐私保护算法

随着城市智能化发展，室内定位技术已成为各类位置服务的重要应用基础。在一些室内应用场景中，服务器端需要在保护用户位置隐私的前提下，完成特定区域的用户访问统计。为此，提出了一种基于布隆过滤器和Paillier同态加密的多级敏感区域室内定位算法，旨在保护用户位置隐私的同时服务器能判断用户是否进入特定区域。算法根据区域的类别或敏感级别对室内进行划分，利用Paillier算法对服务器端和用户端的数据进行加密，设计了一种改进的基于布隆过滤器的算法在密文域完成用户位置的判定，减少了加密运算带来的巨大通信开销与计算开销。在公共数据集上的实验结果表明，与已有的空间布隆过滤器算法相比，提出的哈希数组合并算法在同样的通信和计算开销时具有更低的误判率，也可扩展至其他应用中实现多类数据集的编码。

Privacy preserving algorithm in indoor localization of multilevel sensitive areas

With the development of urban intelligence, indoor positioning has become an important application basis for providing various location-based services. In some indoor application scenarios, the server-side needs to perform user access statistics for specific areas while ensuring the protection of user location privacy. To address this, this paper proposed a multi-level sensitive area indoor positioning algorithm based on Bloom filter and Paillier homomorphic encryption, aiming to protect user location privacy while enabling the server to judge whether a user had entered a sensitive area. The algorithm divided the indoor space based on the sensitivity level or category of areas, encrypted the data on the server-side and user-side using the Paillier algorithm, and designed an improved Bloom filter-based algorithm in the ciphertext domain to accomplish user location determination, thereby reducing the significant communication overhead and computational cost introduced by encryption operations. Experimental results on public data sets show that compared with existing spatial Bloom filter algorithms, the proposed hash array merging algorithm has a lower false positive probability with the same communication and computation overhead, and can also be extended to other applications to realize multi-class data set coding.