Rizvi, S. J. & Haritsa, J. R. (2002), Privacypreserving association rule mining, in `Proceedings of 28th International Conference on Very Large Data Bases(VLDB)'.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....mining has been recently been proposed in response to the above concerns [3, 13] There have been two broad approaches. The randomization approach focuses on individual privacy, and reveals randomized information about each record in exchange for not having to reveal the original records to anyone [1, 3, 10, 18]. In the secure multi party computation approach, the goal is to build a data mining model across multiple databases without revealing the individual records in each database to the other databases [13, 12, 21] In this paper, we focus on privacy breaches in the context of the randomization ....

S. J. Rizvi and J. R. Haritsa. Privacy-preserving association rule mining. In Proc. of the 28th Int'l Conference on Very Large Databases, August 2002.

....learn the value of one record, the problem becomes that of symmetric private information retrieval [25] This literature will be useful for developing protocols for the selection operation in our setting. The problem of privacy preserving data mining is also related. The randomization approach [6, 22, 40] focuses on individual privacy rather than on database privacy, and reveals randomized information about each record in exchange for not having to reveal the original records to anyone. More closely related is the work in [33] on building a decision tree classifier across multiple databases, ....

S. J. Rizvi and J. R. Haritsa. Privacy-preserving association rule mining. In Proc. of the 28th Int'l Conference on Very Large Databases, August 2002.

....mining has been recently been proposed in response to the above concerns [3, 13] There have been two broad approaches. The randomization approach focuses on individual privacy, and reveals randomized information about each record in exchange for not having to reveal the original records to anyone [1, 3, 10, 18]. In the secure multi party computation approach, the goal is to build a data mining model across multiple databases without revealing the individual records in each database to the other databases [13, 12, 21] In this paper, we focus on privacy breaches in the context of the randomization ....

S. J. Rizvi and J. R. Haritsa. Privacy-preserving association rule mining. In Proc. of the 28th Int'l Conference on Very Large Databases, August 2002.

.... for classification, by adding random values from a normal Gaussian distribution of mean 0 to the actual data values) One problem with this approach is a tradeo# between privacy and the accuracy of the results[1] More recently, data perturbation has been applied to boolean association rules [18]. One interesting feature of this work is a flexible definition of privacy; e.g. the ability to correctly guess a value of 1 from the perturbed data can be considered a greater threat to privacy than correctly learning a 0 . 15] use cryptographic protocols to achieve complete zero knowledge ....

S. J. Rizvi and J. R. Haritsa. Privacy-preserving association rule mining. In Proceedings of 28th International Conference on Very Large Data Bases. VLDB, Aug. 20-23 2002.

.... tightened the bounds on what private information is disclosed, by showing that the ability to reconstruct the distribution can be used to tighten estimates of original values based on the distorted data [1] More recently, the data distortion approach has been applied to boolean association rules [10]. Again, the idea is to modify data values such that reconstruction of the values for any individual transaction is di#cult, but the rules learned on the distorted data are still valid. One interesting feature of this work is a flexible definition of privacy; e.g. the ability to correctly guess a ....

S. J. Rizvi and J. R. Haritsa. Privacy-preserving association rule mining. In Proceedings of 28th International Conference on Very Large Data Bases. VLDB, Aug. 20-23 2002.

.... for classification, by adding random values from a normal Gaussian distribution of mean 0 to the actual data values) One problem with this approach is a tradeo# between privacy and the accuracy of the results[1] More recently, data perturbation has been applied to boolean association rules [19]. Here, the idea is to flip 0 s and 1 s so that reconstruction of the values for any individual transaction is di#cult, but the rules learned on the distorted data approximately match the true rules. One interesting feature of this work is a flexible definition of privacy; e.g. the ability to ....

S. J. Rizvi and J. R. Haritsa. Privacy-preserving association rule mining. In Proceedings of 28th International Conference on Very Large Data Bases. VLDB, Aug. 20-23 2002.

No context found.

Rizvi, S. J. & Haritsa, J. R. (2002), Privacypreserving association rule mining, in `Proceedings of 28th International Conference on Very Large Data Bases(VLDB)'.

No context found.

S. J. Rizvi and J. R. Haritsa. Privacy-Preserving Association Rule Mining. In Proc. of the 28th International Conference on Very Large Data Bases, Hong Kong, China, August 2002.

No context found.

S. J. Rizvi and J. R. Haritsa. Privacy-Preserving Association Rule Mining. In Proc. of the 28th International Conference on Very Large Data Bases, Hong Kong, China, August 2002.

No context found.

S. Rizvi, and J. Haritsa. Privacy preserving association rule mining. In Proceedings of 28th International Conference on Very Large Data Bases. Aug, 2002.

No context found.

S. Rizvi, and J. Haritsa. Privacy preserving association rule mining. In Proceedings of 28th International Conference on Very Large Data Bases. Aug, 2002.

No context found.

S. J. Rizvi and J. R. Haritsa. Privacy-preserving association rule mining. In Proc. of the 28th Int'l Conference on Very Large Databases, August 2002.

No context found.

S. J. Rizvi and J. R. Haritsa. Privacy-Preserving Association Rule Mining. In Proc. of the 28th International Conference on Very Large Data Bases, Hong Kong, China, August 2002.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC