Integrating XML data sources using approximate joins
XML is widely recognized as the data interchange standard of tomorrow because of its ability to represent data from a variety of sources. Hence, XML is likely to be the format through which data from multiple sources is integrated. In this article, we study the problem of integrating XML data sources through correlations realized as join operations. A challenging aspect of this operation is the XML document structure. Two documents might convey approximately or exactly the same information but may be quite different in structure. Consequently, an approximate match in structure, in addition to content, has to be folded into the join operation. We quantify an approximate match in structure and content for pairs of XML documents using well defined notions of distance. We show how notions of distance that have metric properties can be incorporated in a framework for joins between XML data sources and introduce the idea of reference sets to facilitate this operation. Intuitively, a reference set consists of data elements used to project the data space. We characterize what constitutes a good choice of a reference set, and we propose sampling-based algorithms to identify them. We then instantiate our join framework using the tree edit distance between a pair of trees. We next turn our attention to utilizing well known index structures to improve the performance of approximate XML join operations. We present a methodology enabling adaptation of index structures for this problem, and we instantiate it in terms of the R-tree. We demonstrate the practical utility of our solutions using large collections of real and synthetic XML data sets, varying parameters of interest, and highlighting the performance benefits of our approach.

title: A compressed accessibility map for XML
abstract: XML is the undisputed standard for data representation and exchange. As companies transact business over the Internet, letting authorized customers directly access, and even modify, XML data offers many advantages in terms of cost, accuracy, and timeliness. Given the complex business relationships between companies, and the sensitive nature of information, access must be provided selectively, using sophisticated access control specifications. Using the specification directly to determine if a user has access to an XML data item can be extremely inefficient. The alternative of fully materializing, for each data item, the users authorized to access it can be space-inefficient. In this article, we introduce a compressed accessibility map (CAM) as a space- and time-efficient solution to the access control problem for XML data. A CAM compactly identifies the XML data items to which a user has access, by exploiting structural locality of accessibility in tree-structured data. We present a CAM lookup algorithm for determining if a user has access to a data item that takes time proportional to the product of the depth of the item in the XML data and logarithm of the CAM size. We develop an algorithm for building an optimal size CAM that takes time linear in the size of the XML data set. While optimality cannot be preserved incrementally under data item updates, we provide an algorithm for incrementally maintaining near-optimality. Finally, we experimentally demonstrate the effectiveness of the CAM for multiple users on a variety of real and synthetic data sets.

title: Index-based approximate XML joins
abstract: XML data integration tools are facing a variety of challenges for their efficient and effective operation. Among these is the requirement to handle a variety of inconsistencies or mistakes present in the data sets. We study the problem of integrating XML data sources through index assisted join operations, using notions of approximate match in the structure and content of XML documents as the join predicate. We show how a well known and widely deployed index structure, namely the R-tree, can be adopted to improve the performance of such operations. We propose novel search and join algorithms for R-trees adopted to index XML document collections. We also propose novel optimization objectives for R-tree construction, making R-trees better suited for this application.

title: Aggregate Query Answering on Anonymized Tables
abstract: Privacy is a serious concern when microdata need to be released for ad hoc analyses. The privacy goals of existing privacy protection approaches (e.g., k-anonymity and l-diversity) are suitable only for categorical sensitive attributes. Since applying them directly to numerical sensitive attributes (e.g., salary) may result in undesirable information leakage, we propose privacy goals to better capture the need of privacy protection for numerical sensitive attributes. Complementing the desire for privacy is the need to support ad hoc aggregate analyses over microdata. Existing generalization-based anonymization approaches cannot answer aggregate queries with reasonable accuracy. We present a general framework of permutation-based anonymization to support accurate answering of aggregate queries and show that, for the same grouping, permutation-based techniques can always answer aggregate queries more accurately than generalization-based approaches. We further propose several criteria to optimize permutations for accurate answering of aggregate queries, and develop efficient algorithms for each criterion.

title: Interoperable strategies in automated trust negotiation
abstract: Automated trust negotiation is an approach to establishing trust between strangers through the exchange of digital credentials and the use of access control policies that specify what combinations of credentials a stranger must disclose in order to gain access to each local service or credential. We introduce the concept of a trust negotiation protocol, which defines the ordering of messages and the type of information messages will contain. To carry out trust negotiation, a party pairs its negotiation protocol with a trust negotiation strategy that controls the exact content of the messages, i.e., which credentials to disclose, when to disclose them, and when to terminate a negotiation. There are a huge number of possible strategies for negotiating trust, each with different properties with respect to speed of negotiations and caution in giving out credentials and policies. In the autonomous world of the Internet, entities will want the freedom to choose negotiation strategies that meet their own goals, which means that two strangers who negotiate trust will often not use the same strategy. To date, only a tiny fraction of the space of possible negotiation strategies has been explored, and no two of the strategies proposed so far will interoperate. In this paper, we define a large set of strategies called the disclosure tree strategy (DTS) family. Then we prove that if two parties each choose strategies from the DTS family, then they will be able to negotiate trust as well as if they were both using the same strategy. Further, they can change strategies at any point during negotiation. We also show that the DTS family is closed, i.e., any strategy that can interoperate with every strategy in the DTS family must also be a member of the DTS family. We also give examples of practical strategies that belong to the DTS family and fit within the TrustBuilder architecture and protocol for trust negotiation.

title: Defining and Measuring Policy Coverage in Testing Access Control Policies
abstract: To facilitate managing access control in a system, security officers increasingly write access control policies in specification languages such as XACML, and use a dedicated software component called a Policy Decision Point (PDP). To increase confidence on written policies, certain types of policy testing (often in an ad hoc way) are usually conducted, which probe the PDP with some typical requests and check PDP's responses against expected ones. This paper develops a first step toward systematic policy testing by defining and measuring policy coverage when testing policies. We have developed a coverage-measurement tool to measure policy coverage given a set of XACML policies and a set of requests. We have developed a tool for request generation, which randomly generates requests for a given set of policies, and a tool for request reduction, which greedily selects a nearly minimal set of requests for achieving the same coverage as the originally generated requests. To evaluate coverage-based request reduction and its effect on fault detection, we have conducted an experiment with mutation testing on a set of real policies. Our experimental results show that the coveragebased test reduction can substantially reduce the size of generated requests and incur only relatively low loss on fault detection. We also conduct a study on the policy coverage achieved by manually generated requests.

title: A field model for human detection and tracking
abstract: The large shape variability and partial occlusions challenge most object detection and tracking methods for nonrigid targets such as pedestrians. This paper presents a new approach based on a two-layer statistical field model that characterizes the prior of the complex shape variations as a Boltzmann distribution and embeds this prior and the complex image likelihood into a Markov field. A probabilistic variational analysis of this model reveals a set of fixed-point equations characterizing the equilibrium of the field. It leads to computationally efficient methods for calculating the image likelihood and for training the model. Based on that, effective algorithms for detecting nonrigid objects are developed. This new approach has several advantages. First, it is intrinsically suitable for capturing local nonrigidity. In addition, due to the distributed likelihood, this approach is robust to partial occlusions. Moreover, the two-layer structure provides large flexibility of modeling the image observations, which makes the new method robust to clutters. Extensive experiments demonstrate its effectiveness.

title: Service selection algorithms for Web services with end-to-end QoS constraints
abstract: Web services are new forms of Internet software that can be universally deployed and invoked using standard protocols. Services from different providers can be integrated into a composite service regardless of their locations, platforms, and/or execution speeds to implement complex business processes and transactions. In this paper, we study the end-to-end QoS issues of composite services by utilizing a QoS broker that is responsible for selecting and coordinating the individual service component. We design the service selection algorithms used by QoS brokers to construct the optimal composite service. The objective of the algorithms is to maximize the user-defined utility function value while meeting the end-to-end delay constraint. We propose two solution approaches to the service selection problem: the combinatorial approach, by modeling the problem as the Multiple Choice Knapsack Problem (MCKP), and the graph approach, by modeling the problem as the constrained shortest path problem in the graph theory. We study efficient solutions for each approach.

title: Modeling and Measuring Privacy Risks in QoS Web Services
abstract: The privacy issue for QoS Web services is considered. As more Web services are constructed and deployed, many of our personal needs may be served by more than one service providers regardless of their platforms and implementation technologies. Service selection is performed by comparing the QoS attributes of all candidate services. Releasing private data without due control may raise the risk of allowing potential adversaries to collect, reveal or utilize one's data in undesirable ways. We propose a QoS model that quantifies a user's privacy risk in order to make the service selection process manageable. The privacy risk is derived using the percentage of the private data set to be released, and user-defined context-oriented weights that quantify the potential damage of privacy leaking in a specific context. We have included privacy risk cost as one of the QoS parameters by extending the end-to-end QoS composition algorithms developed previously

title: Service Selection Algorithms for Web Services with End-to-End QoS Constraints
abstract: Web services are new forms of Internet software that can be universally deployed and invoked using standard protocol. Services from different providers can be integrated to provide composite services. In this paper, we study the end-to-end QoS issues of composite service by utilizing a QoS broker that is responsible for coordinating the individual service component to meet the quality constraint. We design the service selection algorithms used by QoS brokers to meet end-to-end QoS constraints. The objective of the algorithms is to maximize the user-defined utility while meeting the end-to-end delay constraint. We model the problem as the multiple choice knapsack problem (MCKP) and provide efficient solutions. The algorithms are tested for their performance.

title: Collaborative tracking of multiple targets
abstract: Coalescence, meaning the tracker associates more than one trajectories to some targets while loses track for others, is a challenging problem for visual tracking of multiple targets, especially when similar targets move close or present occlusions. Existing approaches that are based on joint data association are confronted by the combinatorial complexity due to the concatenation of the state spaces of individual targets. This paper presents a novel collaborative approach with linear complexity to the coalescence problem. The basic idea is the collaborative inference mechanism, in which the estimate of an individual target is not only determined by its own observation and dynamics, but also through the interaction and collaboration with the estimates of its adjacent targets, which leads to a competition mechanism that enables different targets to compete for the common image observations. The theoretical foundation of the new approach is based on Markov networks. Variational analysis of this Markov network reveals a mean field approximation to the posterior density of each target, therefore provides a computationally efficient way for such a difficult inference problem. In addition, a mean field Monte Carlo (MFMC) algorithm is designed to achieve Bayesian inference by simulating the competition among a set of low dimensional particle filters. Compared with the existing solutions, the proposed new collaborative approach stands out by its effectiveness and low computational cost to the coalescence problem, as pronounced in the extensive experiments.