The privacy aspect of state estimation algorithms has been drawing high research attention due to the necessity for a trustworthy private environment in cyber-physical systems. These systems usually engage cloud-computing platforms to aggregate essential information from spatially distributed nodes and produce desired estimates. The exchange of sensitive data among semi-honest parties raises privacy concerns, especially when there are coalitions between parties. We propose two privacy-preserving protocols using Kalman filter and partially homomorphic encryption of the measurements and estimates while exposing the covariances and other model parameters. We prove that the proposed protocols achieve satisfying computational privacy guarantees against various coalitions based on formal cryptographic definitions of indistinguishability. We evaluated the proposed protocols to demonstrate their efficiency using data from a real testbed.

翻译：国家估算算法的隐私方面一直引起高度的研究关注,因为在网络物理系统中必须有一个值得信赖的私人环境。这些系统通常使用云计算平台,从空间分布的节点中汇总基本信息,并得出理想的估计数。半诚实方之间的敏感数据交流引起了隐私关切,特别是当各方之间有联盟时。我们建议使用Kalman过滤器和部分对测量和估计进行同质加密,同时暴露共变和其他模型参数。我们证明,拟议的协议在基于正式的不可分化加密定义的各种联盟中实现了令人满意的计算隐私权保障。我们评估了拟议的协议,以证明它们使用来自真实测试台的数据的效率。