Using randomized response techniques for privacy-preserving data mining

By Wenliang Du, Justin Zhijun Zhan. Knowledge Discovery and Data Mining, 2003

342 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b94d9bfa0c33b720562dd0d8b8c4143a33e8f95b

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In Artificial Intelligence

Citations in the corpus

Security and Privacy in the Internet of Things (2017, background): “Most existing work falls into this category, which either use cryptographic techniques [107, 143] or privacy-preserving data mining techniques such as value distortion [26], randomized response [61] and partitioned data [144, 63].”
PrivateClean: Data Cleaning and Differential Privacy (2016, result): “This work led to several other results such as algorithms for classiﬁcation on private data [10], statistical reconstruction of a dataset [18].”
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification (2004, methodology): “In general, there are two different approaches: one is to use data distortion approach, in which data are disguised by the added noises [1, 2, 13, 14, 22, 23].”
Privacy-preserving collaborative data mining (2007, background): “[18] showed that these two notions actually coincide.”
Anonymity-preserving data collection (2005, methodology): “Di(cid:11)erent random perturbation methods have been proposed and applied in di(cid:11)erent data mining algorithms [2, 1, 12, 28, 11, 9].”
The applicability of the perturbation based privacy preserving data mining for real-world data (2008, methodology): “The same technique also can be used in decision tree classifier [13].”
Privacy-Preserving Inter-database Operations (2004, methodology): “For example, Du and Zhan have modified the ID3 classification algorithm based on randomized response techniques [12].”
Privacy preserving decision tree learning over multiple parties (2007, methodology): “There have been several approaches to support privacy preserving data mining over multiple private data sources [23] [16] [15] [33] [22] without using third parties.”
Deriving private information from randomized data (2005, methodology): “In the randomization approach, random noises are added to the original data, and only the disguised data are shared [2, 1, 21, 9, 7].”
Privacy-Preserving Backpropagation Neural Network Learning (2009, background): “In particular, privacy-preserving solutions have been proposed for the following classification algorithms (to name a few): decision trees [12], [17], [ 18 ], naive Bayes classifier [19], [20], and support vector machine (SVM) [21]‐[23].”
Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, background): “In a centralized database (C-DB) environment, data are all stored in a single database; while, in a distributed database (D-DB) environment, data are stored in different databases [3]-[6] [13]-[22] [27] [29] [31] .”
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning (2016, methodology): “Furthermore, the proposed scheme prevents the disclosure of the private using homomorphic encryption which has been successfully used for data mining and knowledge discovery such as decision trees [25], [26], Bayesian networks [27], [28], support vector machines [29], and k-means [30].”