Privacy Preserving Location Estimation

这项研究将调查基于位置的服务中的隐私。提供基于位置的服务的第一步是定位用户。这可能会危及用户隐私。在本研究中，我们将开发对位置服务器隐藏用户位置的方法，并且服务器也不知道所提供的服务。我们感兴趣的是开发基于位置的服务，其中位置服务器不能了解用户的位置，但可以组成与位置相关的服务，而不能识别用户获得的服务。此外，用户只能接收与其位置相关的服务，而无需了解整个服务空间。在第一个视图中，这个目标可能是不可能实现的，因为构建服务需要位置信息。然而，最近密码学的进展表明，某些信号处理算法可以在加密域进行。在本研究中，我们将使用两种方法进行加密信号处理。

This research will investigate privacy in location-based services. The first step in providing a location-based service is to locate the user. This might compromise the user privacy. In this research, we will be developing methods in which the location of user is hidden from the location server, and the service provided is also unknown to the server. We are interested in developing location-based services where the location server cannot learn user’s location, but can compose location-related services, while not being able to identify the services obtained by the user. Also, the user can only receive services pertaining to its location, without learning about the whole space of services. In the first view this objective might be a non-starter, as the location information is needed to build the service. However, recent advances in cryptography show that certain signal processing arithmetic can be performed in the encrypted domain. In this research, we will use two methods for encrypted signal processing. As the first method, we will use additive homomorphic cryptosystems. In additive homomorphic cryptosystems, the encrypted addition of two numbers can be calculated by the multiplication of their encrypted values, and the multiplication of two numbers can be calculated by taking the encrypted value of one of them to the power of the other number. Hence, a new algebra is defined in which addition is replaced by multiplication and multiplication is replaced with the power operator. Some other operations such as Euclidean distance measurement and minimization can also be performed at the encrypted domain. We will investigate private location estimation using encrypted signal processing methods. A second group of methods for privacy preserving use randomized embedding, in which data is projected onto a lower dimensional subspace while the distance between adjacent vectors remains constant. Recently, the area of randomized embedding is receiving a lot of attention from compressive sensing and big data community. Lower dimensional embedding can hide the content of data in the higher-dimensional domain. Examples of such mappings are locally sensitive hashing, and locally linear embedding. In this research, we will build localization methods that will provide privacy preserving location estimation using both additive homomorphic cryptosystems and randomized embedding. We will develop our algorithms for client-server applications, and also for multi-party cooperative localization.