A Random Rotation Perturbation Approach to Privacy Preserving Data Classification

By Keke Chen, Ling Liu. 2005

93 citations recorded by the source corpus.

How researchers see this paper

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

1. Otkrist Gupta
2. Ramesh Raskar
3. Ling Liu
4. Abhishek V A
5. Keke Chen
6. Binny S
7. Johan T R
8. Nithin Raj

Citations in the corpus

• Federated Learning: Collaborative Machine Learning without Centralized Training Data (2022, positive): “Another idea is to rotate the datapoints in space by a random rotation matrix [6].”
• Distributed learning of deep neural network over multiple agents (2018, methodology): “Some interesting solutions include using Ada-boost to jointly train classifier ensembles [28], using random rotation perturbations for homomorphic pseudo-encryption [29] and applying homomorphic cryptosystem to perform secure computation [30].”
• A Survey of Multiplicative Perturbation for Privacy-Preserving Data Mining (2008, methodology): “Only recently, the data management community has shown some development on multi-dimensional data perturbation techniques, such as the condensation approach using k-nearest neighbor (kNN) method [1], the multi-dimensional K-anonymization using kd-tree [24], and the multiplicative data perturbation t”
• Privacy preserving data classification with rotation perturbation (2005, methodology): “Due to the space limitation, we will ignore the concrete proofs [4], and summarize that KNN , general Kernel methods, SVM classifiers using polynomial, radial basis, and neural network kernels, and Hyperplane-based classifiers are invariant to rotation.”