The emergence of cloud computing implies and facilitates managing large collections of highly distributed, autonomous, and possibly private databases. While there is an increasing need for services that allow integration and sharing of various data repositories, it remains a challenge to ensure the privacy, interoperability, and scalability for such services. In this paper we demonstrate a scalable and extensible framework that is aimed to enable privacy preserving data federations. The framework is built on top of a distributed mediator-wrapper architecture where nodes can form collaborative groups for secure anonymization and secure query processing when private data need to be accessed. New anonymization models and protocols will be demonstrated that counter potential attacks in the distributed setting.

There is an increasing need for sharing data repositories containing personal information across multiple distributed, possibly untrusted, and private databases. Such data sharing is subject to constraints imposed by privacy of data subjects as well as data confidentiality of institutions or data providers. We developed a set of decentralized protocols that enable data sharing for horizontally partitioned databases given these constraints. Our approach includes a distributed anonymization protocol that allows independent data providers to build a virtual anonymized database, and a distributed querying protocol that allows clients to query the virtual database.

Many contemporary applications rely heavily on large scale distributed and heterogeneous data sources. The key constraints for building a distributed data query infrastructure for such applications are: scalability, consistency, heterogeneity, and network and resource dynamics. We designed and developed DObjects, a general-purpose query and data operations infrastructure that can be integrated with metacomputing middleware. This demo proposal describes the architecture and the dynamic query processing functionalities of our data services and shows how they are integrated with a metacomputing framework offering users an open platform for building distributed applications that require access to data integrated from multiple data sources.

Question-Answer portals such as Naver and Yahoo! Answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. Unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. Furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. Hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. We present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site. We also describe structural differences between question topics that correlate with the success of link analysis for authority discovery. Categories and Subject Descriptors:

Question-Answer portals such as Naver and Yahoo! Answers are growing in popularity. However, despite the increased popularity, the quality of answers is uneven, and while some users usually provide good answers, many others often provide bad answers. Hence, estimating the authority, or the expected quality of users, is a crucial task for this emerging domain, with potential applications to answer ranking and to incentive mechanism design. We adapt a powerful link analysis methodology from the web domain as a first step towards estimating authority in Question Answer portals. Our experimental results over more than 3 million answers from Yahoo! Answers are promising, and warrant further exploration along the lines outlined in this poster.