Privacy-preserving data publishing for horizontally partitioned databases

ABSTRACT

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.

REFERENCES

• 1: P. Jurczyk and L. Xiong. Privacy-preserving data publishing for horizontally partitioned databases. Technical Report TR-2008-013, Emory University, Math&CS Dept., 2008.
• 21: Kristen LeFevre , David J. DeWitt , Raghu Ramakrishnan, Mondrian Multidimensional K-Anonymity, Proceedings of the 22nd International Conference on Data Engineering, p.25, April 03-07, 2006 [doi>10.1109/ICDE.2006.101]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT

General Terms:
Algorithms, Design, Experimentation, Security

Keywords:
data provenance, information integration, probabilistic databases, security and data privacy