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154
The new casper: Query processing for location services without compromising privacy
- IN PROC. OF THE 32ND INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, VLDB
, 2006
"... In this paper, we present a new privacy-aware query processing framework Capser * in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware query processor embedde ..."
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Cited by 233 (7 self)
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In this paper, we present a new privacy-aware query processing framework Capser * in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware query processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user’s cloaked location rather than the exact location. Our proposed privacy-aware query processor is completely independent of how we compute the user’s cloaked location. In other words, any existing location anonymization algorithms that blur the user’s private location into cloaked rectilinear areas can be employed to protect the user’s location privacy. We first propose a privacy-aware query processor that not only supports three new privacy-aware query types, but it also achieves a trade-off between query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our query processor achieves high quality snapshot and continuous location-based services while
SINA: Scalable Incremental Processing of Continuous Queries in Spatio-temporal Databases
- In SIGMOD
, 2004
"... This paper introduces the Scalable INcremental hash-based Algorithm (SINA, for short); a new algorithm for evaluating a set of concurrent continuous spatio-temporal queries. SINA is designed with two goals in mind: (1) Scalability in terms of the number of concurrent continuous spatiotemporal querie ..."
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Cited by 133 (12 self)
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This paper introduces the Scalable INcremental hash-based Algorithm (SINA, for short); a new algorithm for evaluating a set of concurrent continuous spatio-temporal queries. SINA is designed with two goals in mind: (1) Scalability in terms of the number of concurrent continuous spatiotemporal queries, and (2) Incremental evaluation of continuous spatio-temporal queries. SINA achieves scalability by employing a shared execution paradigm where the execution of continuous spatio-temporal queries is abstracted as a spatial join between a set of moving objects and a set of moving queries. Incremental evaluation is achieved by computing only the updates of the previously reported answer. We introduce two types of updates, namely positive and negative updates. Positive or negative updates indicate that a certain object should be added to or removed from the previously reported answer, respectively. SINA manages the computation of positive and negative updates via three phases: the hashing phase, the invalidation phase, and the joining phase. The hashing phase employs an in-memory hash-based join algorithm that results in a set of positive updates. The invalidation phase is triggered every T seconds or when the memory is fully occupied to produce a set of negative updates. Finally, the joining phase is triggered by the end of the invalidation phase to produce a set of both positive and negative updates that result from joining in-memory data with in-disk data. Experimental results show that SINA is scalable and is more e#cient than other index-based spatio-temporal algorithms.
A generic framework for monitoring continuous spatial queries over moving objects
- In SIGMOD
, 2005
"... This paper proposes a generic framework for monitoring continuous spatial queries over moving objects. The frame-work distinguishes itself from existing work by being the first to address the location update issue and to provide a common interface for monitoring mixed types of queries. Based on the ..."
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Cited by 127 (4 self)
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This paper proposes a generic framework for monitoring continuous spatial queries over moving objects. The frame-work distinguishes itself from existing work by being the first to address the location update issue and to provide a common interface for monitoring mixed types of queries. Based on the notion of safe region, the client location update strategy is developed based on the queries being monitored. Thus, it significantly reduces the wireless communication and query reevaluation costs required to maintain the up-to-date query results. We propose algorithms for query eval-uation/reevaluation and for safe region computation in this framework. Enhancements are also proposed to take advan-tage of two practical mobility assumptions: maximum speed and steady movement. The experimental results show that our framework substantially outperforms the traditional pe-riodic monitoring scheme in terms of monitoring accuracy and CPU time while achieving a close-to-optimal wireless communication cost. The framework also can scale up to a large monitoring system and is robust under various object mobility patterns. 1.
Preventing location-based identity inference in anonymous spatial queries
- IEEE Trans. Knowl. Data Eng
"... Abstract—The increasing trend of embedding positioning ca-pabilities (e.g., GPS) in mobile devices facilitates the widespread use of Location Based Services. For such applications to suc-ceed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safe ..."
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Cited by 105 (17 self)
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Abstract—The increasing trend of embedding positioning ca-pabilities (e.g., GPS) in mobile devices facilitates the widespread use of Location Based Services. For such applications to suc-ceed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard commu-nication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical loca-tion of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to Location Based Services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial
MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System
- In EDBT
, 2004
"... Location monitoring is an important issue for real time management of mobile object positions. Significant research efforts have been dedicated to techniques for efficient processing of spatial continuous queries on moving objects in a centralized location monitoring system. Surprisingly, very fe ..."
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Cited by 105 (7 self)
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Location monitoring is an important issue for real time management of mobile object positions. Significant research efforts have been dedicated to techniques for efficient processing of spatial continuous queries on moving objects in a centralized location monitoring system. Surprisingly, very few have promoted a distributed approach to real-time location monitoring. In this paper we present a distributed and scalable solution to processing continuously moving queries on moving objects and describe the design of MobiEyes, a distributed real-time location monitoring system in a mobile environment. Mobieyes utilizes the computational power at mobile objects, leading to significant savings in terms of server load and messaging cost when compared to solutions relying on central processing of location information at the server. We introduce a set of optimization techniques, such as Lazy Query Propagation, Query Grouping, and Safe Periods, to constrict the amount of computations handled by the moving objects and to enhance the performance and system utilization of Mobieyes. We also provide a simulation model in a mobile setup to study the scalability of the MobiEyes distributed location monitoring approach with regard to server load, messaging cost, and amount of computation required on the mobile objects.
A Road Network Embedding Technique for k-Nearest Neighbor Search in Moving Object Databases
- GeoInformatica
, 2002
"... A very important class of queries in GIS applications is the class of K-nearest neighbor queries. Most of the current studies on the K-nearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. In real-world road networks, however, th ..."
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Cited by 90 (5 self)
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A very important class of queries in GIS applications is the class of K-nearest neighbor queries. Most of the current studies on the K-nearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. In real-world road networks, however, the shortest distance between two points depends on the actual path connecting the points and cannot be computed accurately using one of the Minkowski metrics. Thus, the Euclidean distance may not properly approximate the real distance. In this paper, we apply an embedding technique to transform a road network to a high dimensional space in order to utilize computationally simple Minkowski metrics for distance measurement. Subsequently, we extend our approach to dynamically transform new points into the embedding space. Finally, we propose an ef®cient technique that can ®nd the actual shortest path between two points in the original road network using only the embedding space. Our empirical experiments indicate that the Chessboard distance metric …L? † in the embedding space preserves the ordering of the distances between a point and its neighbors more precisely as compared to the Euclidean distance in the original road network.
Nearest neighbor queries in road networks
- In 11th ACM International Symposium on Advances in Geographic Information Systems (GIS’03
, 2003
"... With wireless communications and geo-positioning being widely available, it becomes possible to offer new e-services that provide mobile users with information about other mobile objects. This pa-per concerns active, ordered k-nearest neighbor queries for query and data objects that are moving in ro ..."
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Cited by 59 (8 self)
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With wireless communications and geo-positioning being widely available, it becomes possible to offer new e-services that provide mobile users with information about other mobile objects. This pa-per concerns active, ordered k-nearest neighbor queries for query and data objects that are moving in road networks. Such queries may be of use in many services. Specifically, we present an easily implementable data model that serves well as a foundation for such queries. We also present the design of a prototype system that implements the queries based on the data model. The algorithm used for the nearest neighbor search in the prototype is presented in detail. In addition, the paper reports on results from experiments with the prototype system.
Efficient evaluation of imprecise location-dependent queries
- In Proc. ICDE
, 2007
"... In location-based services, it is common for a user to issue a query based on his/her current position. One such example is “find the available cabs within two miles of my current location”. Very often, the query issuers ’ locations are imprecise due to measurement error, sampling error, or message ..."
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Cited by 41 (2 self)
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In location-based services, it is common for a user to issue a query based on his/her current position. One such example is “find the available cabs within two miles of my current location”. Very often, the query issuers ’ locations are imprecise due to measurement error, sampling error, or message delay. They may also want to protect their privacy by providing a less precise location. In this paper, we study the efficiency of queries that return probabilistic guarantees for location data with uncertainty. We classify this query into two types, based on whether the data (1) has no uncertainty (e.g., shops and restaurants), or (2) has a controlled degree of uncertainty (e.g., moving vehicles). Based on this classification, we develop three methods to improve the computational and I/O performance. The first method expands the query range based on the query issuer’s uncertainty. The second idea exchanges the roles of query and data. The third technique exploits the fact that users may only be interested in answers with probabilities higher than some threshold. Experimental simulation over a realistic dataset reveals that our approaches improve the query performance significantly.
Range nearest-neighbor query
- IEEE Transactions on Knowledge and Data Engineering (TKDE
"... A range nearest-neighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearest-neighbor queries and has many applications. In this paper, we consider the ranges as (hyper-)rectangles and propose efficient in-memory ..."
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Cited by 40 (2 self)
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A range nearest-neighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearest-neighbor queries and has many applications. In this paper, we consider the ranges as (hyper-)rectangles and propose efficient in-memory processing and secondary memory pruning techniques for RNN queries in both 2D and high-dimensional spaces. These techniques are generalized for kRNN queries, which return the k nearest neighbors for every point in the range. In addition, we devise an auxiliary solution-based index EXO-tree to speed up any type of NN query. EXO-tree is orthogonal to any existing NN processing algorithm and thus can be transparently integrated. An extensive empirical study was conducted to evaluate the CPU and I/O performance of these techniques, and the study showed that they are efficient and robust under various datasets, query ranges, numbers of nearest neighbors, dimensions, and cache sizes.
On trip planning queries in spatial databases
- In SSTD
, 2005
"... In this paper we discuss a new type of query in Spatial Databases, called the Trip Planning Query (TPQ). Given a set of points of interest P in space, where each point belongs to a specific category, a starting point S and a destination E, TPQ retrieves the best trip that starts at S, passes through ..."
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Cited by 40 (1 self)
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In this paper we discuss a new type of query in Spatial Databases, called the Trip Planning Query (TPQ). Given a set of points of interest P in space, where each point belongs to a specific category, a starting point S and a destination E, TPQ retrieves the best trip that starts at S, passes through at least one point from each category, and ends at E. For example, a driver traveling from Boston to Providence might want to stop to a gas station, a bank and a post office on his way, and the goal is to provide him with the best possible route (in terms of distance, traffic, road conditions, etc.). The difficulty of this query lies in the existence of multiple choices per category. In this paper, we study fast approximation algorithms for TPQ in a metric space. We provide a number of approximation algorithms with approximation ratios that depend on either the number of categories, the maximum number of points
