Privacy preserving association rule mining in vertically partitioned data

By Jaideep Vaidya, Chris Clifton. 2002

391 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Federated Machine Learning (2019, background): “Vaidya and Clifton proposed secure association mining rules [65], secure k-means [66], and a naïve Bayes classifier [64] for vertically partitioned data.”
Limiting privacy breaches in privacy preserving data mining (2003, methodology): “In the secure multi-party co mputation approach, the goal is to build a data mining model ac ross multiple databases without revealing the individual recor ds in each database to the other databases [13, 12, 21].”
Building decision tree classifier on private data (2002, background): “Although Vaidya and Clifton’s solution has similar communication cost as ours, its computation cost O(n2) is more expensive than ours O(n), where n is the size of the vectors.”
Mining Association Rules under Privacy Constraints (2008, methodology): “Maintaining input data privacy was also considered in [41, 25] 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 deep learning (2015, methodology): “SMC has been used for learning decision trees [33], linear regression functions [17], association rules [50], Naive Bayes classifiers [51], and k-means clustering [28].”
A Communication Efficient Collaborative Learning Framework for Distributed Features (2019, background): “Such a security definition is inline with prior security definitions proposed in privacy-preserving machine learning and secure multiple computation (SMC), such as [27–29, 4, 30].”
Threats to Federated Learning: A Survey (2020, neutral): “VFL is applicable to the cases in which participants have large overlaps in the sample space but differ in the feature space, i.e. , different parties hold different attributes of the same records [Vaidya and Clifton, 2002].”
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.”
Membership Inference Attacks Against Machine Learning Models (2016, methodology): “Secure multiparty computation (SMC) has been used for learning decision trees [21], linear regression functions [9], association rules [28], Naive Bayes classifiers [29], and kmeans clustering [17].”
Privacy preserving frequent itemset mining (2002, background): “In (Vaidya & Clifton 2002), Vaidya and Clifton addressed the problem of association rule mining in which transactions are distributed across sources.”
Privacy-preserving SVM classification (2008, neutral): “Work in PPDM has followed two major directions ‐ the randomization/perturbation approach and the cryptographic approach. Privacy-Preserving SVM Classification 13”
Blockchain-Based Privacy Preserving Deep Learning (2018, background): “…(SMC) can help protect intermediate steps of the computation, such as decision trees [13], linear regression functions [14], association rules [15], Naive Bayes classi-ﬁers [16], and k-means clustering [17],when multiple parties perform collaborative machine learning on their proprietary…”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “rent organizations have the same or similar sample space but differ in the feature space.Vaidya et al. propose multiple secure models on vertically partitioned data, including association rule mining [173], k-means [174], naive bayes classiﬁer [175] and decision tree [176].For the vertical FLS, it u”
L-diversity: Privacy beyond k-anonymity (2007, positive): “These primitives are used in various application scenarios to build multiparty protocols for private association rule mining in horizontally-partitioned data [Kantarcioglu and Clifton 2002], private association rule mining in vertically-partitioned data [Vaidya and Clifton 2002], and private EM clus”
L-diversity: privacy beyond k-anonymity (2006, positive): “These primitives are used in various application scenarios to build multiparty protocols for private association rule mining in horizontally partitioned data [Kantarcioglu and Clifton 2002], private association rule mining in vertically partitioned data [ Vaidya and Clifton 2002 ], and private EM cl”