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Privacy preserving association rule mining
Abstract
The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data, impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to that of the original algorithms.
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Cited by
Privacy preserving association rule mining in vertically partitioned data
Vaidya, J., & Clifton, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775142 · Cited by 1,001
Information Security in Big Data: Privacy and Data Mining
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014) · IEEE Access · doi:10.1109/access.2014.2362522 · Cited by 631
Preserving privacy by de-identifying face images
Newton, E. M., Sweeney, L., & Malin, B. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.32 · Cited by 615
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
Liu, K., Kargupta, H., & Ryan, J. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.14 · Cited by 554
Metadata
- Authors
- Saygin, Y., Verykios, V. S., & Elmagarmid, A. K.
- Venue
- Proceedings Twelfth International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems RIDE-2EC 2002
- Publisher
- IEEE Comput. Soc
- Published
- 2003-06-25
- Pages
- 151-158
- OpenAlex
- W2167411443
- Cited by
- 241
- References
- 0
- FWCI
- 10.56
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Saygin, Y., Verykios, V. S., & Elmagarmid, A. K. (2003). Privacy preserving association rule mining. Proceedings Twelfth International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems RIDE-2EC 2002. https://doi.org/10.1109/ride.2002.995109
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