Privacy preserving data classification with rotation perturbation

By Keke Chen, Ling Liu. Industrial Conference on Data Mining, 2005

348 citations recorded by the source corpus.

Data perturbation techniques are one of the most popular models for privacy preserving data mining (Agrawal and Srikant, 2000; Aggarwal and Yu, 2004). It is especially convenient for applications where the data owners need to export/publish the privacy-sensitive data. A data perturbation procedure can be simply described as follows. Before the data owner publishes the data, they randomly change the data in certain way to disguise the sensitive information while preserving the particular data property that is critical for building the data models. Several perturbation techniques have been proposed recently, among which the most typical ones are randomization approach (Agrawal and Srikant, 2000) and condensation approach (Aggarwal and Yu, 2004).

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/b4a532830bfbf77b9350bcb32659e9b2f340b3a8

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In Artificial Intelligence

Citations in the corpus

Privacy-Preserving Machine Learning Algorithms for Big Data Systems (2015, background): “For example, in [8] and [29], although the kernel is calculated in a privacy-preserving manner (secure dot product), if the kernel matrix is obtained by a learner with more than k training samples, he can calculate all the private training samples of the other learners by solving a set of linear equ”
Cloud-enabled privacy-preserving collaborative learning for mobile sensing (2012, background): “, [14]), the multiplicative noise is dimensionality-preserving while in others [ 32], it is not.”
The applicability of the perturbation based privacy preserving data mining for real-world data (2008, background): “Some other researchers have proposed different noise addition techniques to protect the private data [7] [8] [9].”
Privacy-Preserving Backpropagation Neural Network Learning (2009, background): “If there is a learner trusted by all the data holders, then the trusted learner can collect data ﬁrst and build a learning model.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, background): “In [10], it has been shown that the use of publicly available records can lead to the privacy getting heavily compromised in high-dimensional cases.”
Min Max Normalization Based Data Perturbation Method for Privacy Protection (2011, methodology): “, they proposed a rotation based perturbation method [20].”
A Constraint Satisfaction Cryptanalysis of Bloom Filters in Private Record Linkage (2011, background): “As an alternative to heavyweight SMC, there are approaches to selectively reveal information through transformation [22–25].”