Privacy-Preserving Top-k Spatial Keyword Queries in Untrusted Cloud Environments

Web users and content are increasingly being geo-positioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content. We study the efficient processing of continuously moving top-k spatial keyword (MkSK) queries over spatial keyword data. State-of-the-art solutions for moving queries employ safe zones that guarantee the validity of reported results as long as the user remains within a zone. However, existing safe zone methods focus solely on spatial locations and ignore text relevancy. We propose two algorithms for computing safe zones that guarantee correct results at any time and that aim to optimize the computation on the server as well as the communication between the server and the client. We exploit tight and conservative approximations of safe zones and aggressive computational space pruning. Empirical studies with real data suggest that our proposals are efficient.

With the proliferation of geo-textual objects on the web, extensive efforts have been devoted to improving the efficiency of top- $k$ k spatial keyword queries in different settings. However, comparatively much less work has been reported on enhancing the quality and usability of such queries. In this context, we propose means of enhancing the usability of a top- $k$ k group spatial keyword query, where a group of users aim to find $k$ k objects that contain given query keywords and are nearest to the users. Specifically, when users receive the result of such a query, they may find that one or more objects that they expect to be in the result are in fact missing, and they may wonder why. To address this situation, we develop a so-called why-not query that is able to minimally modify the original query into a query that returns the expected, but missing, objects, in addition to other objects. Specifically, we formalize the why-not query in relation to the top- $k$ k group spatial keyword query, called the W hy-not G roup S patial K eyword Query ( $\mathsf{WGSK}$ WGSK ) that is able to provide a group of users with a more satisfactory query result. We propose a three-phase framework for efficiently computing the $\mathsf{WGSK}$ WGSK . The first phase substantially reduces the search space for the subsequent phases by retrieving a set of objects that may affect the ranking of the user-expected objects. The second phase provides an incremental sampling algorithm that generates candidate weightings of more promising queries. The third phase determines the penalty of each refined query and returns the query with minimal penalty, i.e., the minimally modified query. Extensive experiments with real and synthetic data offer evidence that the proposed solution excels over baselines with respect to both effectiveness and efficiency.

In this paper, we investigate the selectivity estimation problem for streaming spatio-textual data, which arises in many social network and geo-location applications. Specifically, given a set of continuously and rapidly arriving spatio-textual objects, each of which is described by a geo-location and a short text, we aim to accurately estimate the cardinality of a spatial keyword query on objects seen so far, where a spatial keyword query consists of a search region and a set of query keywords. To the best of our knowledge, this is the first work to address this important problem. We first extend two existing techniques to solve this problem, and show their limitations. Inspired by two key observations on the "locality" of the correlations among query keywords, we propose a local correlation based method by utilizing an augmented adaptive space partition tree ( A 2 SP -tree for short) to approximately learn a local Bayesian network on-the-fly for a given query and estimate its selectivity. A novel local boosting approach is presented to further enhance the learning accuracy of local Bayesian networks. Our comprehensive experiments on real-life datasets demonstrate the superior performance of the local correlation based algorithm in terms of estimation accuracy compared to other competitors.

The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword indexing and querying methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose a novel indexing structure called NIQ-tree, which integrates spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare it with other two baseline algorithms.

The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword query methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial Web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose novel indexing structures, which integrate spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare them with other baseline algorithms.

The conventional works on spatial keyword queries for a knowledge base focus on finding a subtree to cover all the query keywords. The retrieved subtree is rooted at a place vertex, spatially close to a query location and compact in terms of the query keywords. However, user requirements may not be satisfied by a single subtree in some application scenarios. A group of subtrees should be combined together to collectively cover the query keywords. In this paper, we propose and study a novel way of searching on a spatial knowledge, namely collective spatial keyword query on a knowledge base (CoSKQ-KB). We formalize the problem of CoSKQ-KB and design a baseline method for CoSKQ-KB (BCK). To further speed up the query processing, an improved scalable method for CoSKQ-KB (iSCK) is proposed based on a set of efficient pruning and early termination techniques. In addition, we conduct empirical experiments on two real-world datasets to show the efficiency and effectiveness of our proposed algorithms.

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.

Spatial objects often come with textual information, such as Points of Interest (POIs) with their descriptions, which are referred to as geo-textual data. To retrieve such data, spatial keyword queries that take into account both spatial proximity and textual relevance have been extensively studied. Existing indexes designed for spatial keyword queries are mostly built based on the geo-textual data without considering the distribution of queries already received. However, previous studies have shown that utilizing the known query distribution can improve the index structure for future query processing. In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload. One key challenge is how to utilize both structured spatial attributes and unstructured textual information during learning the index. We first divide the data objects into partitions, aiming to minimize the processing costs of the given query workload. We prove the NP-hardness of the partitioning problem and propose a machine learning model to find the optimal partitions. Then, to achieve more pruning power, we build a hierarchical structure based on the generated partitions in a bottom-up manner with a reinforcement learning-based approach. We conduct extensive experiments on real-world datasets and query workloads with various distributions, and the results show that WISK outperforms all competitors, achieving up to 8× speedup in querying time with comparable storage overhead.

Level-aware collective spatial keyword queries

Conventional spatial keyword queries confront the difficulty of returning desired objects that are synonyms but morphologically different to query keywords. To overcome this flaw, this paper investigates the interactive spatial keyword querying with semantics. It aims to enhance the conventional queries by not only making sense of the query keywords, but also refining the understanding of query semantics through interactions. On top of the probabilistic topic model, a novel interactive strategy is proposed to precisely infer the latent query semantics by learning from user feedbacks. In each interaction, the returned objects are carefully selected to ensure effective inference of user intended query semantics. Query processing is carried out on a small candidate object set at each round of interaction, and the whole querying process terminates when the latent query semantics learned from user feedback becomes explicit enough. The experimental results on real check-in dataset demonstrates that the quality of results has been significantly improved through limited number of interactions.

Geo-textual data is ubiquitous nowadays, where each object has a location and is associated with some keywords. Many types of queries based on geo-textual data, termed as spatial keyword queries, have been proposed, and are to find optimal object(s) in terms of both its (their) location(s) and keywords. In this paper, we propose a new type of query called nearby-fit spatial keyword query (NSKQ), where an optimal object is defined based not only on the location and the keywords of the object itself, but also on those of the objects nearby. For example, in an application of finding a hotel, not only the location of a hotel but also the objects near the hotel (e.g., shopping malls, restaurants and bus stops nearby) might need to be taken into consideration.The query is proved to be NP-hard, and in order to perform the query efficiently, we developed two approximate algorithms with small constant approximation factors equal to 1.155 and 1.79. We conducted extensive experiments based on both real and synthetic datasets, which verified our algorithms.

Given the prevalence and volume of local search queries, today's search engines are required to find results by both spatial proximity and textual relevance at high query throughput. Existing techniques to answer such spatial keyword queries employ a keyword aggregation strategy that suffers from certain drawbacks when applied to road networks. Instead, we propose the K-SPIN framework, which uses an alternative keyword separation strategy that is more suitable on road networks. While this strategy was previously thought to entail prohibitive pre-processing costs, we further propose novel techniques to make our framework viable and even light-weight. Thorough experimentation shows that K-SPIN outperforms the state-of-the-art by up to two orders of magnitude on a wide range of settings and real-world datasets.

The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals.

Geo-textual indices play an important role in spatial keyword querying. The existing geo-textual indices have not been compared systematically under the same experimental framework. This makes it difficult to determine which indexing technique best supports specific functionality. We provide an all-around survey of 12 state-of-the-art geo-textual indices. We propose a benchmark that enables the comparison of the spatial keyword query performance. We also report on the findings obtained when applying the benchmark to the indices, thus uncovering new insights that may guide index selection as well as further research.

There is more and more commercial and research interest in nearest neighbor objects for location-based search from spatial database. Specially, a spatial keyword query takes a user location and user-supplied keywords as arguments and returns objects that is nearest k objects from user current location and textually relevant to the user required keyword. In these systems, user can type one or more word for required keyword. To find the result that contains at least one required keyword, it is important to support Boolean OR semantic keyword search on spatial database. In this paper we study how to answer such queries efficiently for both kNN query and range query. This paper proposes new index structure that combines K-d tree and inverted file. K-d tree efficiently support for both nearest neighbor and range queries. We also discuss Boolean OR Semantic keyword search for the user’s required keywords.