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Secure Nearest Neighbor Revisited
"... Abstract—In this paper, we investigate the secure nearest neighbor (SNN) problem, in which a client issues an encrypted query point E(q) to a cloud service provider and asks for an encrypted data point in E(D) (the encrypted database) that is closest to the query point, without allowing the server t ..."
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Abstract—In this paper, we investigate the secure nearest neighbor (SNN) problem, in which a client issues an encrypted query point E(q) to a cloud service provider and asks for an encrypted data point in E(D) (the encrypted database) that is closest to the query point, without allowing the server to learn the plaintexts of the data or the query (and its result). We show that efficient attacks exist for existing SNN methods [21], [15], even though they were claimed to be secure in standard security models (such as indistinguishability under chosen plaintext or ciphertext attacks). We also establish a relationship between the SNN problem and the orderpreserving encryption (OPE) problem from the cryptography field [6], [5], and we show that SNN is at least as hard as OPE. Since it is impossible to construct secure OPE schemes in standard security models [6], [5], our results imply that one cannot expect to find the exact (encrypted) nearest neighbor based on only E(q) and E(D). Given this hardness result, we design new SNN methods by asking the server, given only E(q) and E(D), to return a relevant (encrypted) partition E(G) from E(D) (i.e., G ⊆ D), such that that E(G) is guaranteed to contain the answer for the SNN query. Our methods provide customizable tradeoff between efficiency and communication cost, and they are as secure as the encryption scheme E used to encrypt the query and the database, where E can be any wellestablished encryption schemes. I.
Authentication of moving knn queries
 In Proc. ICDE
, 2011
"... Abstract — A moving kNN query continuously reports the k nearest neighbors of a moving query point. In addition to the query result, a service provider that evaluates moving queries often returns mobile clients a safe region that bounds the validity of query results to minimize the communication cos ..."
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Abstract — A moving kNN query continuously reports the k nearest neighbors of a moving query point. In addition to the query result, a service provider that evaluates moving queries often returns mobile clients a safe region that bounds the validity of query results to minimize the communication cost between the two parties. However, when a service provider is not trustworthy, it may send inaccurate query results or incorrect safe regions to clients. In this paper, we present a framework and algorithms to authenticate results and safe regions of moving kNN queries. Extensive experiments on both real and synthetic datasets show that our methods are efficient in terms of both computation time and communication costs. I.
Verifying Spatial Queries Using Voronoi Neighbors
 Proc. 18th SIGSPATIAL Int’l Conf. Advances in Geographic Information Systems
, 2010
"... With the popularity of locationbased services and the abundant usage of smart phones and GPS enabled devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. Nevertheless, in the database outsourcing paradigm, the authentication of the query results at the clien ..."
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With the popularity of locationbased services and the abundant usage of smart phones and GPS enabled devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. Nevertheless, in the database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Database (OSDB) model and propose an efficient scheme, called VNAuth, that allows a client to verify the correctness and completeness of the result set. Our approach can handle both k nearest neighbor (kNN) and range queries, and is based on neighborhood information derived by the Voronoi diagram of the underlying spatial dataset. Specifically, upon receiving a query result, the client can verify its integrity by examining the signatures and exploring the neighborhood of every object in the result set. Compared to the current stateoftheart approaches (i.e., methods based on Merkle hash trees), VNAuth produces significantly smaller verification objects (VO) and is more computationally efficient, especially for queries with low selectivity.
Towards Building A High Performance Spatial Query System for Large Scale Medical Imaging Data
 In SIGSPATIAL/GIS
, 2012
"... Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data and computeinten ..."
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Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data and computeintensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of largescale pathology images generates tremendous amounts of spatially derived quantifications of microanatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous
Reverse k Nearest Neighbors Query Processing: Experiments and Analysis
, 2015
"... Given a set of users, a set of facilities and a query facility q, a reverse k nearest neighbors (RkNN) query returns every user u for which the query is one of its k closest facilities. RkNN queries have been extensively studied under a variety of settings and many sophisticated algorithms have be ..."
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Given a set of users, a set of facilities and a query facility q, a reverse k nearest neighbors (RkNN) query returns every user u for which the query is one of its k closest facilities. RkNN queries have been extensively studied under a variety of settings and many sophisticated algorithms have been proposed to answer these queries. However, the existing experimental studies suffer from a few limitations. For example, some studies estimate the I/O cost by charging a fixed penalty per I/O and we show that this may be misleading. Also, the existing studies either use an extremely small buffer or no buffer at all which puts some algorithms at serious disadvantage. We show that the performance of these algorithms is significantly improved even when a small buffer (containing 100 pages) is used. Finally, in each of the existing studies, the proposed algorithm is mainly compared only with its predecessor assuming that it was the best algorithm at the time which is not necessarily true as shown in our experimental study. Motivated by these limitations, we present a comprehensive experimental study that addresses these limitations and compares some of the most notable algorithms under a wide variety of settings. Furthermore, we also present a carefully developed filtering strategy that significantly improves TPL which is one of the most popular RkNN algorithms. Specifically, the optimized version is up to 20 times faster than the original version and reduces its I/O cost up to two times.
MemoryEfficient Algorithms for Spatial Network Queries
"... Abstract — Incrementally finding thek nearest neighbors (kNN) in a spatial network is an important problem in locationbased services. One method (INE) simply applies Dijkstra’s algorithm. Another method (IER) computes the k nearest neighbors using Euclidean distance followed by computing their corr ..."
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Abstract — Incrementally finding thek nearest neighbors (kNN) in a spatial network is an important problem in locationbased services. One method (INE) simply applies Dijkstra’s algorithm. Another method (IER) computes the k nearest neighbors using Euclidean distance followed by computing their corresponding network distances, and then incrementally finds the next nearest neighbors in order of increasing Euclidean distance until finding one whose Euclidean distance is greater than the current k nearest neighbor in terms of network distance. The LBC method improves on INE by avoiding the visit of nodes that cannot possibly lead to the k nearest neighbors by using a Euclidean heuristic estimator, and on IER by avoiding the repeated visits to nodes in the spatial network that appear on the shortest paths to different members of the k nearest neighbors by performing multiple instances of heuristic search using a Euclidean heuristic
A StateofArt in RTree Variants for Spatial Indexing
"... Nowadays, indexing has become essential for fast retrieval of results. Spatial databases are used in many applications which demand faster retrieval of data. These data are multidimensional. Designing index structure for spatial databases is current area of research. RTree is the most widely used i ..."
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Nowadays, indexing has become essential for fast retrieval of results. Spatial databases are used in many applications which demand faster retrieval of data. These data are multidimensional. Designing index structure for spatial databases is current area of research. RTree is the most widely used index structure for multidimensional data. Many variants of RTree has evolved with each performing better in some aspect like query retrieval, insertion cost, application specific and so on. In this work, stateofart of variants in RTree is presented. This paper provides an idea of the present development in spatial indexing and paves way for the researchers to explore and analyze the difficulties and tradeoffs in the work. The RTree variants are classified according to the way they are different from the original RTree either in the process of construction or whether it is a hybrid of RTree and some other structure or whether it is an extension of RTree to support many other applications.
SLICE: Reviving RegionsBased Pruning for Reverse k Nearest Neighbors Queries
"... Given a set of facilities and a set of users, a reverse k nearest neighbors (RkNN) query q returns every user for which the query facility is one of the kclosest facilities. Due to its importance, RkNN query has received significant research attention in the past few years. Almost all of the exist ..."
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Given a set of facilities and a set of users, a reverse k nearest neighbors (RkNN) query q returns every user for which the query facility is one of the kclosest facilities. Due to its importance, RkNN query has received significant research attention in the past few years. Almost all of the existing techniques adopt a pruningandverification framework. Regionsbased pruning and halfspace pruning are the two most notable pruning strategies. The halfspace based approach prunes a larger area and is generally believed to be superior. Influenced by this perception, almost all existing RkNN algorithms utilize and improve the halfspace pruning strategy. We observe the weaknesses and strengths of both strategies and discover that the regionsbased pruning has certain strengths that have not been exploited in the past. Motivated by this, we present a new RkNN algorithm called SLICE that utilizes the strength of regionsbased pruning and overcomes its limitations. Our extensive experimental study on synthetic and real data sets demonstrate that SLICE is significantly more efficient than the existing algorithms. We also provide a detailed theoretical analysis to analyze various aspects of our algorithm such as I/O cost, the unpruned area, and the cost of its verification phase etc. The experimental study validates our theoretical analysis.
SEQUENTIAL AND MAPREDUCEBASED ALGORITHMS FOR CONSTRUCTING AN INPLACE MULTIDIMENSIONAL QUADTREE INDEX FOR ANSWERING FIXEDRADIUS NEAREST NEIGHBOR QUERIES
"... Abstract. Answering fixedradius nearest neighbor queries constitutes an important problem in many areas, ranging from geographic systems to similarity searching in object databases (e.g. image and video databases). The usual approach in order to efficiently answer such queries is to construct an in ..."
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Abstract. Answering fixedradius nearest neighbor queries constitutes an important problem in many areas, ranging from geographic systems to similarity searching in object databases (e.g. image and video databases). The usual approach in order to efficiently answer such queries is to construct an index. In this paper we present algorithms for constructing a multidimensional quadtree index. We start with wellknown sequential algorithms and then adapt them to the MapReduce computation model, in order to be able to handle large amounts of data. In all the algorithms the objects are indexed in association with quadtree cells (or nodes) which they intersect (plus possibly a few other nearby cells). When processing a query, multiple quadtree cells may be searched in order to find the answer.
L1 topk nearest neighbor searching with uncertain queries
, 2013
"... In this paper, we present algorithms for the topk nearest neighbor searching where the input points are exact and the query point is uncertain under the L1 metric in the plane. The uncertain query point is represented by a discrete probability distribution function, and the goal is to efficiently ..."
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In this paper, we present algorithms for the topk nearest neighbor searching where the input points are exact and the query point is uncertain under the L1 metric in the plane. The uncertain query point is represented by a discrete probability distribution function, and the goal is to efficiently return the topk expected nearest neighbors, which have the smallest expected distances to the query point. Given a set of n exact points in the plane, we build an O(n logn log log n)size data structure in O(n logn log logn) time, such that for any uncertain query point with m possible locations and any integer k with 1 ≤ k ≤ n, the topk expected nearest neighbors can be found in O(m logm + (k + m) log² n) time. Even for the special case where k = 1, our result is better than the previously best method (in PODS 2012), which requires O(n log² n) preprocessing time, O(n log² n) space, and O(m² log³ n) query time. In addition, for the onedimensional version of this problem, our approach can build an O(n)size data structure in O(n logn) time that can support O(min{k, logm} ·m+ k+ log n) time queries and the query time can be reduced to O(k+m+ logn) time if the locations of Q are given sorted. In fact, the problem is equivalent to the aggregate or group nearest neighbor searching with the weighted Sum as the aggregate distance function operator.