Multi party computations: past and present
PODC '97 Proceedings of the sixteenth annual ACM symposium on Principles of distributed computing
The state of the art in distributed query processing
ACM Computing Surveys (CSUR)
ACM Transactions on Computer Systems (TOCS)
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards heterogeneous multimedia information systems: the Garlic approach
RIDE '95 Proceedings of the 5th International Workshop on Research Issues in Data Engineering-Distributed Object Management (RIDE-DOM'95)
Scaling heterogeneous databases and the design of Disco
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Privacy and Ownership Preserving of Outsourced Medical Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy-enhancing k-anonymization of customer data
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymizing sequential releases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Challenging research issues in data mining, databases and information retrieval
ACM SIGKDD Explorations Newsletter
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Centralized and Distributed Anonymization for High-Dimensional Healthcare Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Data sharing in networked environments: organization, platforms and issues
CIT'11 Proceedings of the 5th WSEAS international conference on Communications and information technology
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More and more applications rely heavily on large amounts of data in the distributed storages collected over time or produced by large scale scientific experiments or simulations. An important fact is that many organizations collect, store, and use various types of information about individuals. In consequence, such data sharing is subject to constraints imposed by privacy of individuals or data subjects as well as data confidentiality of institutions or data providers. Given a query spanning multiple databases, it should be executed transparently and efficiently. And most importantly, the results should not contain individually identifiable information and institutions should not reveal their databases to each other apart from the query results. In this paper, we propose a distributed anonymization protocol that allows independent data providers to build a virtual anonymized database from horizontally partitioned databases, and a secure query protocol that allows clients to query those virtual databases. We also propose a distributed data sharing and integration architecture for querying these distributed heterogeneous and possibly private databases. Our system provides efficient and scalable privacy-preserving query execution interface that integrates data seamlessly and transparently.