Search Results (4)

The rapid advancement of the Internet of Things (IoT) and mobile devices has made location-based services (LBSs) increasingly prevalent, significantly improving daily convenience and work efficiency. However, this widespread usage has raised growing concerns about privacy and security, particularly during data outsourcing to cloud servers, where users’ location information and related data are susceptible to breaches by malicious actors or attackers. Traditional privacy-preserving spatial keyword schemes often employ Bloom filters for data encoding and storage. While Bloom filters offer high lookup speeds, they suffer from limitations such as a relatively high false positive rate in certain scenarios and poor space efficiency. These issues can adversely affect query accuracy and overall user experience. Furthermore, existing schemes have not sufficiently addressed the multi-attribute characteristics of spatial textual data. At the same time, relying solely on cloud servers for large-scale data processing introduces additional challenges, including heavy computational overhead, high latency, and substantial communication costs. To address these challenges, we propose a cloud–edge collaborative privacy-preserving dynamic spatial keyword query scheme with multi-attribute cost constraints. This scheme introduces a novel index structure that leverages security-enhanced Xor filter technology and Geohash techniques. This index structure not only strengthens query security and efficiency but also significantly reduces the false positive rate, thereby improving query accuracy. Moreover, the proposed scheme supports multi-attribute cost constraints and dynamic data updates, allowing it to adapt flexibly to practical requirements and user-specific needs. Finally, through security analysis and experimental evaluation, we demonstrate that the proposed scheme is both secure and effective. Full article

Location-based application services and location privacy protection solutions are often required for the storage, management, and efficient retrieval of large amounts of geolocation data for specific locations or location intervals. We design a hierarchical tree-like organization structure, GL-Tree, which enables the storage, management, and retrieval of massive location data and satisfies the user’s location-hiding requirements. We first use Geohash encoding to convert the two-dimensional geospatial coordinates of locations into one-dimensional strings and construct the GL-Tree based on the Geohash encoding principle. We gradually reduce the location intervals by extending the length of the Geohash code to achieve geospatial grid division and spatial approximation of user locations. The hierarchical tree structure of GL-Tree reflects the correspondence between Geohash codes and geographic intervals. Users and their location relationships are recorded in the leaf nodes at each level of the hierarchical GL-Tree. In top–down order, along the GL-Tree, efficient storage and retrieval of location sets for specified locations and specified intervals can be achieved. We conducted experimental tests on the Gowalla public dataset and compared the performance of the B+ tree, R tree, and GL-Tree in terms of time consumption in three aspects: tree construction, location insertion, and location retrieval, and the results show that GL-Tree has good performance in terms of time consumption. Full article

Statistical partitioning and publishing is commonly used in location-based big data services to address queries such as the number of points of interest, available vehicles, traffic flows, infected patients, etc., within a certain range. Adding noise perturbation to the location-based statistical data according to the differential privacy model can reduce various risks caused by location privacy leakage while keeping the statistical characteristics of the published data. The traditional statistical partitioning and publishing methods realize the decomposition and indexing of 2D space from top to bottom. However, they can easily cause the over-partitioning or under-partitioning phenomenon, and therefore need multiple times of data scan. This paper proposes a grid clustering and differential privacy protection method for location-based statistical big data publishing scenarios. We implement location-based big data statistics in units of equal-sized grids and perform density classification on uniformly distributed grids by discrete wavelet transform. A bottom-up grid clustering algorithm is designed to perform on the blank and the uniform grids of the same density level based on neighborhood similarity. The Laplacian noise is incorporated into the clustering results according to the differential privacy model to form the published statistics. Experimental comparison of the real-world datasets manifests that the grid clustering and differential privacy publishing method proposed in this paper is superior to other existing partition publishing methods in terms of range querying accuracy and algorithm operating efficiency. Full article

With the rapid expansion of high-rise and high-density buildings in urban areas, visual privacy has become one of the major concerns affecting human environmental quality. Evaluation of residents’ visual exposure to outsiders has attracted more attention in the past decades. This paper presents a quantitative indicator; namely, the Potential Visual Exposure Index (PVEI), to assess visual privacy by introducing the damage of potential visual incursion from public spaces and neighborhoods in high-density residences. The method for computing the PVEI mainly consists of three steps: extracting targets and potential observers in a built environment, conducting intervisibility analysis and identifying visible sightlines, and integrating sightlines from building level and ground level to compute the PVEI value of each building opening. To validate the proposed PVEI, a case study with a sample building located at the center of Kowloon, Hong Kong, was evaluated. The results were in accordance with the common-sense notion that lower floors are subjected to poor visual privacy, and privacy is relatively well-preserved in upper floors in a building. However, residents of middle floors may suffer the worst circumstances with respect to visual privacy. The PVEI can be a useful indicator to assess visual privacy and can provide valuable information in architectural design, hotel room selection, and building management. Full article