Privacy-preserving k-means clustering over vertically partitioned data

By Jaideep Vaidya, Chris Clifton. 2003

828 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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

In Management Science and Operations Research

Citations in the corpus

Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, background): “The motivation of gleaning insights from vertically partitioned data dates back to association rule mining Vaidya and Clifton (2002, 2003).”
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.”
Privacy Preserving Data Mining (2015, background): “The problem is not data mining, but the way data mining is done [7].”
A Random Rotation Perturbation Approach to Privacy Preserving Data Classification (2005, background): “A considerable amount of work on privacy preserving data mining methods have been reported in recent years [1, 3, 5, 2, 8, 19], etc.”
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data (2008, methodology): “[26] proposed the first method for clustering over vertically partitioned data – a privacy-preserving protocol perform do k-means clustering.”
Privacy-preserving decision trees over vertically partitioned data (2005, methodology): “…and Clifton [2002, 2004], EM clustering in horizontally partitioned data [Lin et al. 2005], K-means clustering in vertically partitioned data [Vaidya and Clifton 2003; Jagannathan and Wright 2005], association rules in vertically partitioned data Vaidya and Clifton [2005b, 2002], and…”
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].”
Anonymity-preserving data collection (2005, background): “Privacy-preserving data mining solutions have also been proposed based on cryptographic techniques (c.f. [23, 34, 38, 39]).”
Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, background): “Horizontal distributions refer to the cases where different records of the same data attributes are resided in different places [7] - [11] [26] ; while in a vertical data distribution, different attributes of the same record of data are resided in different places [2] [23] [24] [25] [30] [33] .”
The applicability of the perturbation based privacy preserving data mining for real-world data (2008, neutral): “Different solutions were given to address different data mining problems using cryptographic techniques (e.g., [15] [16] [17] [18]).”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, methodology): “These protocols are directly applied for privacy preserving association rule mining from vertically partitioned data [32] and horizontally partitioned data [33], clustering with distributed EM mixture modeling [34], and K-Means clustering over vertically partitioned data [ 35 ].”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “propose multiple secure models on vertically partitioned data, including association rule mining [173], k-means [174], naive bayes classifier [175] and decision tree [176].”
Privacy-Preserving Inter-database Operations (2004, background): “Vaidya and Clifton have addressed privacy preservation in k-means clustering (a technique to group items into k clusters) [22].”