Abstract
Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service: a company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the outsourced database and the knowledge extract from it by data mining are considered private property of the data owner. To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing a data mining task within a corporate privacy-preserving framework. We propose a scheme for privacy-preserving outsourced mining which offers a formal protection against information disclosure, and show that the data owner can recover the correct data mining results efficiently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., and R. Srikant. “Fast Algorithms for Mining Association Rules.” (Paper Presented at the Annual International Conference on Very Large Data Bases, 1994).
Agrawal, R., and R. Srikant. “Privacy-Preserving Data Mining.” (Paper Presented at the Annual International Conference SIGMOD, 2000).
Agrawal, R., J. Kiernan, R. Srikant, Y. Xu. “Order Preserving Encryption for Numeric Data.” (Paper Presented at the Annual International Conference SIGMOD, 2004).
Agrawal, S., and J.R. Haritsa. “A Framework for High-Accuracy Privacy-Preserving Mining.” (Paper Presented at the Annual International Conference on Data Engineering, 2005).
Atzori, M., F. Bonchi, F. Giannotti, and D. Pedreschi. “Anonymity Preserving Pattern Discoery.” VLDB Journal 17, (2008): 703.
Bu, S., L.V.S. Lakshmanan, R.T. Ng, and G. Ramesh. “Preservation of Patterns and Input-Output Privacy.” (Paper Presented at the Annual International Conference on Data Engineering, 2007).
Buyya, R., C.S. Yeo and S. Venugopal. “Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities.” (Paper presented at the annual International Conference on High Performance Computing and Communications, 2008).
Chen, K., G. Sun, and L. Liu. “Toward Attack-Resilient Geometric Data Perturbation.” (Paper Presented at the Annual SIAM International Conference on Data Mining, 2007).
Clifton, C., M. Kantarcioglu, and J. Vaidya. “Defining Privacy for Data Mining.” (Paper Presented at the International NSF Workshop on Next Generation Data Mining, 2002).
Fung, B.C.M., K. Wang, and P.S. Yu. “Anonymizing Classification Data for Privacy Preservation.” TKDE 19, (2007): 711.
Gilburd, B., A. Schuster and R. Wolff. “A New Privacy Model and Association-Rule Mining Algorithm for Large-Scale Distributed Environments.” (Paper Presented at the Annual International Conference on Very Large Data Bases, 2005).
Hacigumus, H., B. Iyer, and S. Mehrotra. “Providing database as a service.” (Paper Presented at the Annual International Conference ICDE, 2002).
Kantarcioglu, M., and C. Clifton. “Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data.” TKDE 16, (2004): 1026.
Krishna Prasad, P., and C. Pandu Rangan. “Privacy Preserving BIRCH Algorithm for Clustering Over Vertically Partitioned Databases.” Secure Data Management, (2006).
Lakshmanan, L.V.S., R.T. Ng, and G. Ramesh. “To Do or Not To Do: The Dilemma of Disclosing Anonymized Data.” (Paper Presented at the Annual International Conference on SIGMOD, 2005).
Machanavajjhala, A., J. Gehrke, and D. Kifer. “l-diversity: Privacy Beyond k-anonymity.” (Paper Presented at the Annual International Conference on Data Engineering, 2006).
Rizvi, S., and J.R. Haritsa. “Maintaining Data Privacy in Association Rule Mining.” (Paper Presented at the Annual International Conference on Very Large Data Bases, 2002).
Samarati, P. “Protecting Respondents’ Identities in Microdata Release.” TKDE 13, (2001): 1010.
Song, D.X., D. Wagner, and A. Perrig. “Practical Techniques for Searches on Encrypted Data.” (Paper Presented at the IEEE Symposium on Security and Privacy, 2000).
Wong, W.K., D.W. Cheung, E. Hung, B. Kao, and N. Mamoulis. “Security in Outsourcing of Association Rule Mining.” (Paper Presented at the Annual International Conference on Very Large Data Bases, 2007).
Xiao, X., and Y. Tao. “Anatomy: Simple and Effective Privacy Preservation.” (Paper Presented at the Annual International Conference on Very Large Data Bases, 2006).
Xu, Y., K. Wang, A.W. Fu, P.S. Yu. “Anonymizing Transaction Databases for Publication.” (Paper Presented at the Annual International Conference on Knowledge Discovery and Data Mining 2008).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Giannotti, F., Lakshmanan, L.V., Monreale, A., Pedreschi, D., Wang, H.(. (2011). Privacy-Preserving Data Mining from Outsourced Databases. In: Gutwirth, S., Poullet, Y., De Hert, P., Leenes, R. (eds) Computers, Privacy and Data Protection: an Element of Choice. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0641-5_19
Download citation
DOI: https://doi.org/10.1007/978-94-007-0641-5_19
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-0640-8
Online ISBN: 978-94-007-0641-5
eBook Packages: Humanities, Social Sciences and LawLaw and Criminology (R0)