Privacy-preserving data mining: why, how, and when

By Jaideep Vaidya, Chris Clifton. 2004

How researchers see this paper

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

1. Alan F. Karr
2. Xiaodong Lin
3. Ashish P. Sanil
4. Jerome P. Reiter
5. Slawomir Goryczka
6. Li Xiong
7. Vaidy S. Sunderam
8. Josep Domingo-Ferrer

Citations in the corpus

• A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, methodology): “To compute secure sum, we applied efficient techniques based on threshold homomorphic functions [17] and random shares [52].”
• Secure multiparty aggregation with differential privacy: a comparative study (2013, methodology): “For computing sum, we use efficient techniques based on threshold homomorphic functions [12] and random shares [42].”
• Privacy-Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products (2009, background): “In the computer science literature, privacy-preserving data mining (PPDM) has emerged as a promising approach in a variety of contexts (Agrawal and Srikant 2000; Clifton et al. 2003b; Lindell and Pinkas 2000; Vaidya and Clifton 2004).”