Data collaborations allow users to draw upon diverse resources to solve complex problems. While collaborations enable a greater ability to manipulate data and services, they also create new security vulnerabilities. Collaboration participants need methods to detect suspicious behaviors (potentially

Many applications, including provenance and some analyses of social networks, require path-based queries over graphstructured data. When these graphs contain sensitive information, paths may be broken, resulting in uninformative query results. This paper presents innovative techniques that give users more informative graph query results; the techniques leverage a common industry practice of providing what we call surrogates: alternate, less sensitive versions of nodes and edges releasable to a broader community. We describe techniques for interposing surrogate nodes and edges to protect sensitive graph components, while maximizing graph connectivity and giving users as much information as possible. In this work, we formalize the problem of creating a protected account G' of a graph G. We provide a utility measure to compare the informativeness of alternate protected accounts and an opacity measure for protected accounts, which indicates the likelihood that an attacker can recreate the topology of the original graph from the protected account. We provide an algorithm to create a maximally useful protected account of a sensitive graph, and show through evaluation with the PLUS prototype that using surrogates and protected accounts adds value for the user, with no significant impact on the time required to generate results for graph queries.

OpenII (openintegration.org) is a collaborative effort to create a suite of open-source tools for information integration (II). The project is leveraging the latest developments in II research to create a platform on which integration tools can be built and further research conducted. In addition to a scalable, extensible platform, OpenII includes industrial-strength components developed by MITRE, Google, UC-Irvine, and UC-Berkeley that interoperate through a common repository in order to solve II problems. Components of the toolkit have been successfully applied to several large-scale US government II challenges.

This demonstration presents Galaxy, a schema manager that facilitates easy and correct data sharing among autonomous but related, evolving data sources. Galaxy reduces heterogeneity by helping database developers identify, reuse, customize, and advertise related schema components. The central idea is that as schemata are customized, Galaxy maintains a derivation graph, and exploits it for data exchange, discovery, and multi-database query over the "galaxy" of related data sources. Using a set of schemata from the biomedical domain, we demonstrate how Galaxy facilitates schema and data sharing.

Data integration systems often provide a uniform query interface, called amediated schema, to a multitude of data sources. To answer user queries, such systems employ a set ofsemantic matchesbetween the mediated schema and the data-source schemas. Finding such matches is well known to be difficult. Hence much work has focused on developing semi-automatic techniques to efficiently find the matches. In this paper we consider the complementary problem ofimproving the mediated schema, to make finding such matches easier. Specifically, a mediated schemaSwill typically be matched with many source schemas. Thus,can the developer of S analyze and revise S in a way that preserves S's semantics, and yet makes it easier to match with in the future? In this paper we provide an affirmative answer to the above question, and outline a promising solution direction, calledmSeer. Given a mediated schemaSand a matching toolM,mSeerfirst computes a matchability score that quantifies how wellScan be matched against usingM. Next,mSeeruses this score to generate a matchability report that identifies the problems in matchingS.Finally,mSeeraddresses these problems by automatically suggesting changes toS(e.g., renaming an attribute, reformatting data values, etc.) that it believes will preserve the semantics ofSand yet make it more amenable to matching. We present extensive experiments over several real-world domains that demonstrate the promise of the proposed approach.

Many AI and other applications populate their knowledge-bases with information retrieved from large, shared databases. This paper describes a new approach to maintaining consistency between objects in dynamic, shared databases and copies of those objects which are cached in an application knowledge-base. The approach relies on an intelligent interface to active databases that we call a Mediator for Approximate Consistency (MAC). The MAC has several unique features: (1) it permits applications to specify their consistency requirements declaratively, using a simple extension of a frame-based representation language, (2) it automatically generates the interfaces and database objects necessary to enforce those consistency requirements, shielding the knowledge-base developer from the implementation details of consistency maintenance, and (3) it provides an explicit representation of consistency constraints in the database, which allows them to be queried and reasoned about. The paper describes the knowledge-base/database consistency problem and previous approaches to dealing with it. It then describes our architecture for maintaining approximate knowledge-base/database consistency, including techniques for specifying, representing, and enforcing consistency constraints.