Group Nearest Neighbor Queries Research Articles

Secure and Practical Group Nearest Neighbor Query for Location-Based Services in Cloud Computing

Group nearest neighbor (GNN) query enables a group of location-based service (LBS) users to retrieve a point from point of interests (POIs) with the minimum aggregate distance to them. For resource constraints and privacy concerns, LBS provider outsources the encrypted POIs to a powerful cloud server. The encryption-and-outsourcing mechanism brings a challenge for the data utilization. However, as previous work from k − anonymity technique leaks all contents of POIs and returns an answer set with redundant communication cost, the LBS system cannot work properly with those privacy-preserving schemes. In this paper, we illustrate a secure group nearest neighbor query scheme, which is referred to as SecGNN. It supports the GNN query with n n ≥ 3 LBS users and assures the data privacy and query privacy. Since SecGNN only achieves linear search complexity, an efficiency enhanced scheme (named Sec GNN + ) is introduced by taking advantage of the KD-tree data structure. Specifically, we convert the GNN problem to the nearest neighbor problem for their centroid, which can be computed by anonymous veto network and Burmester–Desmedt conference key agreement protocols. Furthermore, the Sec GNN + scheme is introduced from the KD-tree data structure and a designed tool, which supports the computation of inner products over ciphertexts. Finally, we run experiments on a real-database and a random database to evaluate the performance of our SecGNN and Sec GNN + schemes. The experimental results show the high efficiency of our proposed schemes.

Cohesive Group Nearest Neighbor Queries on Road-Social Networks under Multi-Criteria

The group nearest neighbor (GNN) search on a road network <inline-formula><tex-math notation="LaTeX">$G_r$</tex-math></inline-formula> , i.e., finding the spatial objects as activity assembly points with the smallest sum of distances to query users on <inline-formula><tex-math notation="LaTeX">$G_r$</tex-math></inline-formula> , has been extensively studied; however, previous works neglected the fact that social relationships among query users, which ensure the maximally favorable atmosphere in the activity, can play an important role in GNN queries. Meanwhile, the ratings of spatial objects can also be used as recommended guidelines. Many real-world applications, such as location-based social networking services, require such queries. In this paper, we study two new problems: (1) a GNN search on a road network that incorporates cohesive social relationships (CGNN) and (2) a CGNN query under multi-criteria (MCGNN). Specifically, both the query users of highest closeness and the corresponding top- <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> objects are retrieved. To address critical challenges on the effectiveness of results and the efficiency of computation over large road-social networks: (1) for CGNN, we propose a filtering-and-verification framework. During filtering, we prune substantial unpromising users and objects using social and geospatial constraints. During verification, we obtain the object candidates, among which the top <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> are selected, with respect to the qualified users; (2) for MCGNN, we propose threshold-based selection and expansion strategies, where different strict boundaries are proposed to ensure that correct top- <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> objects are found early. Moreover, we further optimize search strategies to improve query performance. Finally, experimental results on real social and road networks significantly demonstrate the efficiency and efficacy of our solutions.

A New Group Location Privacy-Preserving Method Based on Distributed Architecture in LBS

Nowadays, the location privacy problem has become an important problem for the users who enjoy the location-based services (LBSs). Researchers have focused on the problem of how to protect the location privacy of user efficiently for a long time. On one hand, many achievements adopt the centralized structure in which there is an additional center server. Additionally, some other researchers adopt the distributed structure to overcome the disadvantages brought by the center server in the centralized anonymous system structure. On the other hand, the existing methods of solving the problem are always to protect the individual user’s location privacy in LBSs, without considering the user group’s location privacy. This kind of methods is not very applicable to the status of a number of users who formed a group to complete a LBS task together by collaborative computing. In order to solve the problem of location privacy protection for a user group in the untrusted mobile social networks, a location privacy protection method based on the distributed structure is discussed in this paper. In the scheme, the special homomorphic features of BGN cryptosystem are cleverly used so that it can solve the group’s three classical location service applications simultaneously, namely, group nearest neighbor query, optimal group collection point determination, and group friend’s distance query, by only one security policy. If there are k users who formed the group, it could achieve k-anonymity without exposing the coordinate of each individual user or using any anonymous areas. Furthermore, theoretical and experimental analysis proves that the proposal can efficiently protect each user’s location privacy in the group through taking full advantage of the collaborative computing and communication capabilities of the mobile terminals. It can resist the existing distance interaction attack and collusion attack and can realize the secure and efficient fine-grained controllable location privacy protection for the user group.

Protecting privacy for distance and rank based group nearest neighbor queries

This paper proposes a novel approach to safeguarding location privacy for GNN (group nearest neighbor) queries. Given the locations of a group of dispersed users, the GNN query returns the location that minimizes the total or the maximal distance for all group users. The returned location is typically a meeting place such as a cinema or coffee shop where the group would like to meet. In our work, we highlight the challenges for private GNN queries and propose a general framework that have two key features: (i) it ensures privacy in a decentralized manner and (ii) can compute an optimal location for GNN query that maximizes the group’s overall preference for the meeting place. To mask their precise locations, we assume that user locations are given as regions to a location-based service provider (LSP). The LSP computes then a set of candidate answers (i.e., meeting places) for the GNN query. We identify two privacy attacks on the user locations, the distance intersection attack and the rank disclosure attack. These attacks are possible when the answer of a GNN query is determined from the candidate answers in a straightforward manner. We develop private filters that prevent these attacks and compute the GNN from the retrieved candidate answers. Our decentralized approach ensures that neither the users nor the LSP can learn the location of any group member. Our algorithms compute from the candidate set an optimal meeting place given the group members’ imprecise locations. Our key insight to an efficient computation is to prune the meeting places that cannot be GNNs given the locations of the group members within the search region. A comprehensive experimental evaluation shows the effectiveness of our approach to answering private GNN queries.

Efficient privacy-preserving group-nearest-neighbor queries with the presence of active adversaries

Location-based services (LBSs) allow users to ask location-dependent queries and receive information based on their location. A group of users can send a group-nearest-neighbor (GNN) query in order to receive a Point Of Interest (POI). This POI in turn shows a point which is the minimum distance from all members of the group. To benefit from these services, it is important to preserve the location privacy of each group user from others in the group (Intragroup location privacy) as well as from anyone outside of the group, including the LBS, (Intergroup location privacy). It may also be necessary to protect the location privacy of the resulting POI from the LBS and other possible attackers. In this paper, we propose two different privacy-preserving protocols for finding the exact answer to a GNN query among a set of returned POIs. The first protocol assumes a semi-honest model while the second one works in a malicious model. The proposed protocols are based on the Anonymous Veto network and Burmester–Desmedt key establishment protocols. The security analysis shows that the proposed protocols provide both Intragroup and Intergroup location privacy; they also protect the location privacy of the resulting POI and are resistant to collusion and multi-point aggregate distance attacks. The performed analyses indicate that they incur a constant computation cost per user and are efficient in terms of computation and communication costs.

A resource-aware approach for authenticating privacy preserving GNN queries

Nowadays many location service providers (LSPs) employ spatial databases outsourced from a third-party data owner (DO) to answer various users’ queries, e.g., group nearest neighbor (GNN) queries that enable a group of users to find a meeting place minimizing their aggregate travel distance. Along with the benefits from LSPs and DO, protection of location privacy and authentication of query results become two major concerns for users while assessing GNN queries. This paper proposes a resource-aware approach that supports effective location privacy preservation and efficient query result authentication with a less storage, communication and computation overhead. Specifically, two centroid-based techniques are investigated to generate a centroid point, which initiates GNN query on behalf of the group members. Then, an authentication algorithm based on Voronoi diagram is proposed for spatial queries. Finally, we demonstrate how our approach is resistant to various attacks, and evaluate its performance by comparing with three competitive approaches. The results show the proposed approach is better and more economical in terms of resource overhead, while considering both privacy preservation and query authentication.

Monochromatic and bichromatic reverse top-k group nearest neighbor queries

The Group Nearest Neighbor (GNN) search is an important approach for expert and intelligent systems, i.e., Geographic Information System (GIS) and Decision Support System (DSS). However, traditional GNN search starts from users’ perspective and selects the locations or objects that users like. Such applications fail to help the managers since they do not provide managerial insights. In this paper, we focus on solving the problem from the managers’ perspective. In particular, we propose a novel GNN query, namely, the reverse top-k group nearest neighbor (RkGNN) query which returns k groups of data objects so that each group has the query object q as their group nearest neighbor (GNN). This query is an important tool for decision support, e.g., location-based service, product data analysis, trip planning, and disaster management because it provides data analysts an intuitive way for finding significant groups of data objects with respect to q. Despite their importance, this kind of queries has not received adequate attention from the research community and it is a challenging task to efficiently answer the RkGNN queries. To this end, we first formalize the reverse top-k group nearest neighbor query in both monochromatic and bichromatic cases, and then propose effective pruning methods, i.e., sorting and threshold pruning, MBR property pruning, and window pruning, to reduce the search space during the RkGNN query processing. Furthermore, we improve the performance by employing the reuse heap technique. As an extension to the RkGNN query, we also study an interesting variant of the RkGNN query, namely a constrained reverse top-k group nearest neighbor (CRkGN) query. Extensive experiments using synthetic and real datasets demonstrate the efficiency and effectiveness of our approaches.

Group nearest-neighbor queries in the [formula omitted] plane

Let P be a set of n points in the plane. The k-nearest-neighbor (abbreviated as k-NN) query problem is to preprocess P into a data structure that quickly reports k closest points in P for a query point q. This paper addresses a generalization of the k-NN query problem to a query set Q of points, namely, the group k-nearest-neighbor query problem, in the L1 plane. More precisely, a query is assigned with a set Q of at most m points and a positive integer k with k≤n, and the distance between a point p of P and a query set Q is defined as the sum of L1 distances from p to all q∈Q. The maximum number m of query points Q is assumed to be known in advance and to be at most n. In this paper, we propose two algorithms, one based on the range tree and the other based on a data structure for segment dragging queries, and obtain the following complexity bounds: (1) a group k-NN query can be handled in O(Tminlog⁡n+(k+m2)(log⁡log⁡n+log⁡m)) time after preprocessing P using O(m2nlog2⁡n) space, where Tmin=min⁡{k+m,m2}, or (2) a group k-NN query can be handled in O((k+m)log2⁡n+m2(logϵ⁡n+log⁡m)) time after preprocessing P using O(m2n) space, where ϵ>0 is an arbitrarily small constant. We also show that our approach can be applied to the weighted group k-nearest-neighbor query problem and the group k-farthest-neighbor query problem.