Privacy-preserving distributed mining of association rules on horizontally partitioned data

By Murat Kantarcioglu, Chris Clifton. 2004

1,163 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.

In Artificial Intelligence

Citations in the corpus

Federated Machine Learning (2019, background): “The authors of [31] proposed an algorithm for association rules on horizontally partitioned data.”
Privacy Preserving Data Mining (2015, background): “One of the most important topics in research community is Privacy Preserving Data Mining (PPDM).”
Tools for privacy preserving distributed data mining (2002, methodology): “We have since developed techniques for association rules[12; 17], clustering, k-nearest neighbor classification, and are working on others.”
Secret Sharing vs . Encryption-based Techniques For Privacy Preserving Data Mining 1 (2008, background): “Kantarcioglu and Clifton developed protocols to privately mine for association rules in a distributed environment[12].”
Privacy-preserving SVM classification (2008, positive): “Specifically , secure methods have been proposed for association rule mining [18, 36 ], clustering[37, 38, 39], classification [40, 41, 42, 43], outlier detection [44] and regression[45, 46].”
Communication-Efficient Edge AI: Algorithms and Systems (2020, methodology): “…server to avoid the exposure of users data [116], [117] • Vertical architecture for privacy with vertically partitioned data and model [19], [118], [119], [120], [121], [122], [123] Computation Ofﬂoading Based Edge Inference Systems Server-based edge inference: • Partial data transmission for…”
Privacy-Preserving Set Union (2007, methodology): “Furthermore, PPSU has been used as a building block in some privacy-preserving data mining protocols [16,23] and privacy-preserving graph algorithm protocols [1], and thus we believe that improved protocols for PPSU will be useful in many application domains.”
Context-Aware Verifiable Cloud Computing (2017, positive): “Kantarcioglu and Clifton [10] proposed two protocols to implement privacy-preserving mining of association rules over horizontally partitioned data.”
Privacy-Preserving Machine Learning for Speech Processing (2012, neutral): “…preserving data mining such as decision trees [Vaidya et al., 2008a], set matching [Freedman et al., 2004b], clustering [Lin et al., 2005], association rule mining [Kantarcioglu and Clifton, 2004], naive Bayes classification [Vaidya et al., 2008b], support vector machines [Vaidya et al., 2008c].”
Privacy preserving decision tree learning over multiple parties (2007, background): “Vaidya and Clifton [33] studied privacy preserving distributed association rule mining over vertically partitioned data and Kantarcioglu and Clifton [22] studied the privacy preserving association rule mining over horizontally partitioned data using cryptographic tools such as secure multi-party com”
Threats to Federated Learning: A Survey (2020, background): “Under HFL, datasets owned by each participant share similar features but concern different users [Kantarcioglu and Clifton, 2004].”
Privacy preserving frequent itemset mining (2002, background): “In (Clifton, Du, Atallah, Kantarcioglu, Lin & Vaidya 2001), Clifton et al. argued that there is no exact solution that resolves this privacy problem Copyright c©2002, Australian Computer Society, Inc.”
A Framework for Evaluating Privacy Preserving Data Mining Algorithms* (2005, methodology): “A cryptography-based technique is proposed by Kantarcioglu and Clifton (2002).”
Privacy-Preserving Machine Learning Algorithms for Big Data Systems (2015, background): “The latter is known as data mining over horizontally partitioned data [17], where the entire table is partitioned by rows and each learner has a certain number of rows with the same number of features.”
A framework for high-accuracy privacy-preserving mining (2004, background): “Maintaining input data privacy is considered in [17, 25, 26, 27] in the context of databases that are distributed across a number of sites with each site only willing to share data mining results, but not the source data.”
Privacy-Preserving Stochastic Gradual Learning (2018, background): “This issue is very common for privacy-preserving algorithms [24,25,26,27,11].”
Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data (2004, methodology): “This concept was first proposed for decision trees[8]; protocols have also been developed for association rules[11, 6] and clustering[12, 7].”