A Framework for Evaluating Privacy Preserving Data Mining Algorithms*

By Elisa Bertino, Igor Nai Fovino, Loredana Parasiliti Provenza. 2005

224 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jaideep Vaidya
2. Ashraf El-Sisi
3. Chris Clifton
4. Elisa Bertino
5. Yu Zhu
6. Dianmin Yue
7. Yunfeng Wang
8. Fengli Liu

In Management Science and Operations Research

1. Jaideep Vaidya
2. Chris Clifton
3. Ashraf El-Sisi
4. Yu Zhu
5. Dan Lin
6. Elisa Bertino
7. Dianmin Yue
8. Yunfeng Wang

Citations in the corpus

• Privacy Preserving Data Mining (2015, methodology): “The framework they identified was based on the following evaluation dimensions [9]: -- Efficiency.”
• Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, methodology): “Moreover, data mining techniques can reveal critical information about business transactions, compromising the free competition in a business setting [1].”
• Fast Cryptographic Privacy Preserving Association Rules Mining on Distributed Homogenous Database (2008, methodology): “In [12] measuring the quality of data which a privacy preserving algorithm is applied is closely related to the information loss resulting from changing the original data when applying the new algorithm.”
• A Survey of Quantification of Privacy Preserving Data Mining Algorithms (2008, background): “in [6].”