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。这可能会损害用户隐私。在这项研究中，我们将开发的方法，其中用户的位置是隐藏的位置服务器，提供的服务也是未知的服务器。我们有兴趣开发基于位置的服务，位置服务器不能学习用户的位置，但可以组成位置相关的服务，而不能识别用户获得的服务。此外，用户只能接收与其位置有关的服务，而不能了解整个服务空间。在第一种观点中，这个目标可能是不可行的，因为构建服务需要位置信息。然而，密码学的最新进展表明，某些信号处理算法可以在加密域中执行。在本研究中，我们将使用两种方法进行加密信号处理。 作为第一种方法，我们将使用加法同态密码系统。在加法同态密码系统中，两个数字的加密加法可以通过它们的加密值相乘来计算，而两个数字的乘法可以通过将其中一个数字的加密值乘以另一个数字的幂来计算。因此，定义了一个新的代数，其中加法用乘法代替，乘法用幂算子代替。还可以在加密域处执行诸如欧几里德距离测量和最小化的一些其他操作。我们将研究使用加密信号处理方法的私人位置估计。 第二组隐私保护方法使用随机嵌入，其中数据被投影到低维子空间上，而相邻向量之间的距离保持不变。最近，随机嵌入领域受到压缩感知和大数据社区的广泛关注。低维嵌入可以隐藏高维域中的数据内容。这种映射的例子是局部敏感散列和局部线性嵌入。在这项研究中，我们将建立本地化的方法，将提供隐私保护的位置估计使用加性同态密码系统和随机嵌入。我们将开发我们的客户端-服务器应用程序的算法，也为多方合作定位。

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.