Abstract
There is increasing need to build information systems that protect the privacy and ownership of data without impeding the flow of information. We will present some of our current work to demonstrate the technical feasibility of building such systems.
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Agrawal, R., Srikant, R.: Privacy Preserving Data Mining. In: ACM Int’l Conf. on Management of Data (SIGMOD), Dallas, Texas (May 2000)
Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Hippocratic Databases. In: 28th Int’l Conf. on Very Large Data Bases (VLDB), Hong Kong (August 2002)
Agrawal, R., Evfimievski, A., Srikant, R.: Information Sharing Across Private Databases. In: Boulicaut, J.-F., et al. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, p. 8. Springer, Heidelberg (2004)
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Agrawal, R. (2004). Data Privacy. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Machine Learning: ECML 2004. ECML 2004. Lecture Notes in Computer Science(), vol 3201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30115-8_2
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DOI: https://doi.org/10.1007/978-3-540-30115-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23105-9
Online ISBN: 978-3-540-30115-8
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