As the location-based applications ourishing, we will witness soon the transferring of a prodigious amount of data from the local to a public cloud. The rising demand for outsourced data is moving toward a wider geographical area with arbitrary distribution (i.e., dense or sparse) and query scope (i.e., limited or vast). In terms of cloud risks, the outsourced individual data should be preserved when being queried, especially for location information. Geometric range queries are one of the most fundamental search functions. However, the existed works of secure geometric queries are far from practical usage on efciency and security simultaneously. In this paper, we propose a novel scheme, LuxGeo. Our scheme reaches a constant navigation and a linear sweep, which is tailored for secure and efcient location-lookup. Our experiments over three real-world spatial datasets have shown its practical efciency. For example, it only takes 10.01s with 728 tuples retrieved over 63, 369 ciphertext dataset for a single query. LuxGeo has better performance than the existed solutions for a GSE problem on efciency and security.

With the increasing popularity of location-based services (LBS), data outsourcing toward clouds is an emerging paradigm for ease of data management by LBS providers. Geometric range queries are one of the fundamental search functions in LBS, which are to find points inside geometric areas (e.g., circles or polygons). To ensure data confidentiality, the service users tend to encrypt the data before outsourcing it. However, regarding encrypted data, only a few consider geometric range queries, where the rationale is the high-dimension calculations make these queries particularly harder. In this paper, we propose a novel scheme for geometric range queries, that can provide the privacy of data stored at a cloud server and queries. Our scheme supports querying encrypted spatial data with irregular-shaped areas, achieves fast searches and enables dynamic updates. Experimental results over real-world spatial datasets demonstrate that our scheme results in fewer communication rounds and can speed up the search time 4× compared to state-of-the-art schemes, without carrying any potentially visible leakage in the structure.

Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differentially private federated learning has been intensively studied. The existing works are mainly based on the curator model or local model of differential privacy. However, both of them have pros and cons. The curator model allows greater accuracy but requires a trusted analyzer. In the local model where users randomize local data before sending them to the analyzer, a trusted analyzer is not required but the accuracy is limited. In this work, by leveraging the \textit{privacy amplification} effect in the recently proposed shuffle model of differential privacy, we achieve the best of two worlds, i.e., accuracy in the curator model and strong privacy without relying on any trusted party. We first propose an FL framework in the shuffle model and a simple protocol (SS-Simple) extended from existing work. We find that SS-Simple only provides an insufficient privacy amplification effect in FL since the dimension of the model parameter is quite large. To solve this challenge, we propose an enhanced protocol (SS-Double) to increase the privacy amplification effect by subsampling. Furthermore, for boosting the utility when the model size is greater than the user population, we propose an advanced protocol (SS-Topk) with gradient sparsification techniques. We also provide theoretical analysis and numerical evaluations of the privacy amplification of the proposed protocols. Experiments on real-world dataset validate that SS-Topk improves the testing accuracy by 60.7% than the local model based FL. We highlight an observation that SS-Topk improves the accuracy by 33.94\% than the curator model based FL without any trusted party. Compared with non-private FL, our protocol SS-Topk only lose 1.48% accuracy under (2.348, 5e-6)-DP per epoch.

Group k-nearest neighbor (kGNN) search allows a group of n mobile users to jointly retrieve k points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving kGNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every user's location should be protected from other users in the group. We design two privacy preserving solutions under two types of threat model to the privacy preserving kGNN search in the full user collusion environment, where any n - 1 users in the group may collude to infer the location of the remaining user. Our solutions do not rely on heavy pre-computation on LSP like previous works. Though we consider kGNN, the proposed privacy preserving solutions can be easily adopted to any group query as it treats the query answering (i.e., kGNN) as a black box. Theoretical and experimental analysis suggest that our solutions are highly efficient in both communication cost and user computational cost while incurring some reasonable overhead on LSP.

As the location-based applications are flourishing, we will witness soon a prodigious amount of spatial data will be stored in the public cloud with the geometric range query as one of the most fundamental search functions. The rising demand of outsourced data is moving larger-scale datasets and wider-scope query size. To protect the confidentiality of the geographic information of individuals, the outsourced data stored at the cloud server should be preserved especially when they are queried. While the problem of secure range query on outsourced encrypted data has been extensively studied, the current schemes are far from the practice in terms of efficiency and scalability. In this paper, we propose a novel solution based on Geohash and predicate symmetric searchable encryption for secure range queries named as MixGeo. We present a multi-level indexes structure tailored for efficient and large-scale spatial data lookup in the cloud server while preserving data privacy.