连续空间查询的位置隐私保护综述

在移动互联网中,移动用户把个人位置信息发送到位置服务提供者并通过空间查询获取兴趣点数据;在连续空间查询中,用户沿着轨迹提交多个位置信息,这导致了严重的用户隐私风险。近年来,连续空间查询的位置隐私保护技术成为无线网络安全和隐私领域的研究热点。首先介绍了位置服务中的空间查询和隐私威胁;归纳了连续空间查询的隐私保护模型,并比较了对应的隐私保护方法。最后介绍了发展趋势并指明了未来的研究方向。     

空间数据库中的障碍反向最近邻查询

反向最近邻查询已成为空间查询的热点问题,而障碍物在实际应用中是不可避免的,因而在障碍物环境中的反向最近邻查询也成为重要的空间查询。已有的可视反向最近邻查询只考虑了可视性,并没有考虑最小障碍距离。提出一种障碍物环境中新的反向最近邻查询的变体,查找障碍距离最小的反向最近邻,即障碍反向最近邻查询。利用障碍距离的计算和相应的剪枝规则,给出障碍反向最近邻查询的算法及相关定理和证明。     

An efficient method for privacy preserving location queries

Recently, the issue of privacy preserving location queries has attracted much research. However, there are few works focusing on the tradeoff between location privacy preservation and location query information collection. To tackle this kind of tradeoff, we propose the privacy persevering location query (PLQ), an efficient privacy preserving location query processing framework. This framework can enable the location-based query without revealing user location information. The framework can also facilitate location-based service providers to collect some information about the location based query, which is useful in practice. PLQ consists of three key components, namely, the location anonymizer at the client side, the privacy query processor at the server side, and an additional trusted third party connecting the client and server. The location anonymizer blurs the user location into a cloaked area based on a map-hierarchy. The map-hierarchy contains accurate regions that are partitioned according to real landforms. The privacy query processor deals with the requested nearest-neighbor (NN) location based query. A new convex hull of polygon (CHP) algorithm is proposed for nearest-neighbor queries using a polygon cloaked area. The experimental results show that our algorithms can efficiently process location based queries.     

A taxonomy for moving object queries in spatial databases

In the past decade, many works have focused on the development of moving object database indexing and querying. Most of those works have concentrated on the common spatial queries which are used with static objects as well. However, moving objects have different features from static objects which may lead to a variety of queries. Therefore, it is important to understand the full spectrum of moving object queries, even before starting to build an index structure for such objects. The aim of this paper is to provide a complete picture of the capabilities of moving object queries. Thus motivated, in this paper we propose a taxonomy of moving object queries, comprising five perspectives: (i) Location perspective, (ii) Motion perspective, (iii) Object perspective, (vi) Temporal perspective and (v) Patterns perspective. These give an overall view of what moving object queries are about. In this work, each perspective is described and examples are given.     

Efficient location area planning for cellular networks with hierarchical location databases

Location area planning (LAP) is an important issue in the design of high-performance PCS networks. It could have a serious impact on the total mobility management cost of mobile terminals. Most of the previous works either explored the LAP problem as a 0–1 linear programming problem or used adopted techniques, such as simulated annealing, taboo search, and genetic algorithms [IEEE Trans. Vehicular Technol. 49 (2000) 1678; Proceedings of 1999 Vehicular Technology Conference, vol. 4, 1999, pp. 2119–2123; IEEE Vehicular Technol. Conf. 3 (1996) 1835; Proceedings of IEEE INFOCOM'01, Anchorage, Alaska, April 2001; IEEE Trans. Vehicular Technol. 47 (1998) 987], to derive a solution to minimize the location update cost. In this paper, we model and resolve the LAP problem as a set-covering problem. The main advantage of this approach is that it can adapt to the changing mobility patterns of the mobile terminals. We propose the set-covering-based location area planning (SCBLP) algorithm to minimize the total number of location updates, in which the cost-benefit functions are defined based on the coupling and cohesive functions among neighboring cells. We then apply SCBLP to the location database system with a hierarchical structure to further improve the overall system performance in searching and updating the location databases. Extensive simulation experiments have been conducted, and the experimental results show that our proposed algorithms can significantly reduce the location management costs, compared to the greedy algorithm and the random algorithm.     

New plane-sweep algorithms for distance-based join queries in spatial databases

Efficient and effective processing of the distance-based join query (DJQ) is of great importance in spatial databases due to the wide area of applications that may address such queries (mapping, urban planning, transportation planning, resource management, etc.). The most representative and studied DJQs are the K Closest Pairs Query (KCPQ) and εDistance Join Query (εDJQ). These spatial queries involve two spatial data sets and a distance function to measure the degree of closeness, along with a given number of pairs in the final result (K) or a distance threshold (ε). In this paper, we propose four new plane-sweep-based algorithms for KCPQs and their extensions for εDJQs in the context of spatial databases, without the use of an index for any of the two disk-resident data sets (since, building and using indexes is not always in favor of processing performance). They employ a combination of plane-sweep algorithms and space partitioning techniques to join the data sets. Finally, we present results of an extensive experimental study, that compares the efficiency and effectiveness of the proposed algorithms for KCPQs and εDJQs. This performance study, conducted on medium and big spatial data sets (real and synthetic) validates that the proposed plane-sweep-based algorithms are very promising in terms of both efficient and effective measures, when neither inputs are indexed. Moreover, the best of the new algorithms is experimentally compared to the best algorithm that is based on the R-tree (a widely accepted access method), for KCPQs and εDJQs, using the same data sets. This comparison shows that the new algorithms outperform R-tree based algorithms, in most cases.     

Blind evaluation of location based queries using space transformation to preserve location privacy

In this paper we propose a fundamental approach to perform the class of Range and Nearest Neighbor (NN) queries, the core class of spatial queries used in location-based services, without revealing any location information about the query in order to preserve users’ private location information. The idea behind our approach is to utilize the power of one-way transformations to map the space of all objects and queries to another space and resolve spatial queries blindly in the transformed space. Traditional encryption based techniques, solutions based on the theory of private information retrieval, or the recently proposed anonymity and cloaking based approaches cannot provide stringent privacy guarantees without incurring costly computation and/or communication overhead. In contrast, we propose efficient algorithms to evaluate KNN and range queries privately in the Hilbert transformed space. We also propose a dual curve query resolution technique which further reduces the costs of performing range and KNN queries using a single Hilbert curve. We experimentally evaluate the performance of our proposed range and KNN query processing techniques and verify the strong level of privacy achieved with acceptable computation and communication overhead.     

Preference queries in deductive databases

Traditional database query languages such as datalog and SQL allow the user to specify only mandatory requirements on the data to be retrieved from a database. In many applications, it may be natural to express not only mandatory requirements but also preferences on the data to be retrieved. Lacroix and Lavency¹⁰⁾ extended SQL with a notion of preference and showed how the resulting query language could still be translated into the domain relational calculus. We explore the use of preference in databases in the setting of datalog. We introduce the formalism of preference datalog programs (PDPs) as preference logic programs without uninterpreted function symbols for this purpose. PDPs extend datalog not only with constructs to specify which predicate is to be optimized and the criterion for optimization but also with constructs to specify which predicate to be relaxed and the criterion to be used for relaxation. We can show that all of the soft requirements in Reference¹⁰) can be directly encoded in PDP. We first develop anaively-pruned bottom-up evaluation procedure that is sound and complete for computing answers to normal and relaxation queries when the PDPs are stratified, we then show how the evaluation scheme can be extended to the case when the programs are not necessarily stratified, and finally we develop an extension of themagic templates method for datalog¹⁴) that constructs an equivalent but more efficient program for bottom-up evaluation. Kannan Govindarajan, Ph.D.: He obtained his bachelors degree in Computer Science and Engineering from the Indian Institute of Technology, Madras, and he completed his Ph.D. degree in Computer Science from the State University of New York at Buffalo. His dissertation research was on optimization and relaxation techniques for logic languages. His interests lie in the areas of programming languages, databases, and distributed systems. He currently leads the trading community effort in the E-speak Operation in Hewlett Packard Company. Prior to that, he was a member of the Java Products Group in Oracle Corporation. Bharat Jayaraman, Ph.D.: He is a Professor in the Department of Computer Science at the State University of New York at Buffalo. He obtained his bachelors degree in Electronics from the Indian Institute of Technology, Madras (1975), and his Ph.D. from the University of Utah (1981). His research interests are in programming languages and declarative modeling of complex systems. Dr. Jayaraman has published over 50 papers in refereed conferences and journals. He has served on the program committees of several conferences in the area of programming languages, and he is presently on the Editorial Board of the Journal of Functional and Logic Programming. Surya Mantha, Ph.D.: He is a manager in the Communications and Software Services Group of Pittiglio Rabin Todd & McGrath (PRTM), a management consulting firm serving high technology industries. He obtained a bachelors degree in Computer Science and Engineering from the Indian Institute of Technology, Kanpur, an MBA in Finance and Competitive Strategy from the University of Rochester, and a Ph.D. in Computer Science from the University of Utah (1991). His research interests are in the modeling of complex business processes, inter-enterprise application integration, and business strategy. Dr. Mantha has two US patents, and has published over 10 research papers. Prior to joining PRTM, he was a researcher and manager in the Architecture and Document Services Technology Center at Xerox Corporation in Rochester, New York.     

数据库中不等式查询语句的resilience计算

针对数据库中不等式连接查询的因果关系问题,引入并实现了resilience计算,并且为了降低其在路径类型不等式连接查询中计算的时间复杂度,提出了求解resilience的动态规划（DPResi）算法。首先,根据路径类型不等式连接查询的特点及最大流最小割原理,实现了多项式时间复杂度的Min-Cut算法;然后通过将带有不等式布尔连接查询语句的溯源表达式编辑为溯源图,进而将resilience求解问题转换为溯源图中最短距离的计算问题,并结合溯源图的包含关系与最优子结构性质,运用动态规划的思想实现了线性时间复杂度的DPResi算法。在TPC-H数据集上进行了大量实验,实验结果表明,与Min-Cut算法相比,DPResi算法极大地提高了resilience计算的效率,并具有较好的扩展性。     

空间众包中的位置隐私保护技术综述

群众外包(crowdsourcing,简称众包)是互联网发展带来的新的商业模式,依赖大量的工作者完成任务。许多众包任务都是在线完成的,其中存在一类特殊的任务依赖于用户实际的位置信息,这类依赖于用户实际位置信息的众包通常被称为空间众包。近年来,随着移动设备和无线网络的迅速发展,传感器能够更加精确的获取用户的位置、移动速度和方向等信息,空间众包中用户的位置隐私安全问题日益凸显。本文集中介绍空间众包的基本概念,工作流程以及已有空间众包平台中的位置隐私保护问题,并以此为基础详细阐述了空间众包中基于差分隐私、空间匿名以及加密技术的三种主流的隐私保护模型。最后总结并展望了我们未来的研究方向。     

Domain-independent queries on databases with external functions

We study queries over databases with external functions, from a language-independent perspective. The input and output types of the external functions can be atomic values, flat relations, nested relations, etc. We propose a new notion of data-independence for queries on databases with external functions, which extends naturally the notion of generic queries on relational databases without external functions. In contrast to previous such notions, ours can also be applied to queries expressed in query languages with iterations. Next, we propose two natural notions of computability for queries over databases with external functions, and prove that they are equivalent, under reasonable assumptions. Thus, our definition of computability is robust. Finally, based on this equivalence result, we give examples of complete query languages with external functions. A byproduct of the equivalence result is the fact that Relational Machines (Abiteboul and V. Vianu, 1991; Abiteboul et al., 1992) are complete on nested relations: they are known not to be complete on flat relations.     

Supporting quantified queries in distributed databases

We show that some relational queries, which we call quantified queries are not well supported in distributed environments. We give a formal definition of quantified queries, propose a language in which to express said queries and provide a procedure to compute answers in this new language in the context of distributed databases. The proposed language is made up of high-level, declarative operators (called generalised quantifiers), and therefore it can be used in combination with several distributed frameworks. Our approach is designed to be as general as possible; it assumes horizontally partitioned relations, but nothing else, so no data placement or replication is used. We present an implementation and algorithms for the new language, propose some basic optimisations and give experimental results which show that the new approach is indeed quite efficient and scales well.     

Optimizing join queries in distributed databases

A reduced cover set of the set of full reducer semijoin programs for an acyclic query graph for a distributed database system is given. An algorithm is presented that determines the minimum cost full reducer program. The computational complexity of finding the optimal full reducer for a single relation is of the same order as that of finding the optimal full reducer for all relations. The optimization algorithm is able to handle query graphs where more than one attribute is common between the relations. A method for determining the optimum profitable semijoin program is presented. A low-cost algorithm which determines a near-optimal profitable semijoin program is outlined. This is done by converting a semijoin program into a partial order graph. This graph also allows one to maximize the concurrent processing of the semijoins. It is shown that the minimum response time is given by the largest cost path of the partial order graph. This reducibility is used as a post optimizer for the SSD-1 query optimization algorithm. It is shown that the least upper bound on the length of any profitable semijoin program is N(N-1) for a query graph of N nodes     

On contextual ranking queries in databases

In this paper, we identify a novel and interesting type of queries, contextual ranking queries, which return the ranks of query tuples among some context tuples given in the queries. Contextual ranking queries are useful for olap and decision support applications in non-traditional data exploration. They provide a mechanism to quickly identify where tuples stand within the context. In this paper, we extend the sql language to express contextual ranking queries and propose a general partition-based framework for processing them. In this framework, we use a novel method that utilizes bitmap indices built on ranking functions. This method can efficiently identify a small number of candidate tuples, thus achieves lower cost than alternative methods. We analytically investigate the advantages and drawbacks of these methods, according to a preliminary cost model. Experimental results suggest that the algorithm using bitmap indices on ranking functions can be substantially more efficient than other methods.     

Evaluating recursive queries in distributed databases

The execution of logic queries in a distributed database environment is studied. Conventional optimization strategies, such as the early evaluation of selection conditions and the clustering of processing to manipulate and exchange large sets of tuples, are redefined in view of the additional difficulties due to logic queries, in particular to recursive rules. In order to allow efficient processing of these logic queries, several program transformation techniques that attempt to minimize distribution costs based on the idea of semijoins and generalized semijoins in conventional databases are presented. Although local computation of semijoins is not possible for the general case, classes of programs are indicated for which these transformations succeed in producing set-oriented computation. Processes evaluating the recursive program in a distributed network are described, and an efficient method for testing the termination of the computation is developed. The approach is compared with sequential as well as dataflow-oriented evaluation     

A fully spatial personalized differentially private mechanism to provide non-uniform privacy guarantees for spatial databases

Spatial databases are essential to applications in a wide variety of domains. One of the main privacy concerns when answering statistical queries, such as range counting queries, over a spatial database is that an adversary observing changes in query answers may be able to determine whether or not a particular geometric object is present in the database. Differential privacy addresses this concern by guaranteeing that the presence or absence of a geometric object has little effect on query answers. Most of the current differentially private mechanisms for spatial databases ignore the fact that privacy is personal and, thus, provide the same privacy protection for all geometric objects. However, some particular geometric objects may be more sensitive to privacy issues than others, requiring stronger differential privacy guarantees. In this paper, we introduce the concept of spatial personalized differential privacy for spatial databases where different geometric objects have different privacy protection requirements. Also, we present SPDP-PCE, a novel spatial personalized differentially private mechanism to answer range counting queries over spatial databases that fully considers the privacy protection requirements of geometric objects in the underlying geometric space in both steps of noise addition and consistency enforcement. Our experimental results on real datasets demonstrate the effectiveness of SPDP-PCE under various total privacy budgets, query shapes, and privacy level distributions.     

Partial evaluation of queries in deductive databases

This paper presents some applications of partial evaluation method to a query optimization in deductive database. A Horn clause transformation is used for the partial evaluation of a query in an intensional database, and its application to multiple query processing is discussed. Three strategies are presented for the compatible case, ordered case and crossed case. In each case, partial evaluation is used to preprocess the intensional database in order to obtain subqueries which direct access to an extensional database.