Privacy-preserving distributed k-means clustering over arbitrarily partitioned data

By Geetha Jagannathan, Rebecca N. Wright. 2005

476 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Privacy preservation in federated learning: An insightful survey from the GDPR perspective (2020, methodology): “Indeed, SMC has been employed in numerousML algorithms such as secure two-party computation (S2C) in linear regression [31], Iterative Dichotomiser3 (ID3) decision tree learning algorithm [78], and k-means clustering algorithm for distributed data mining [62].”
SecureML: A System for Scalable Privacy-Preserving Machine Learning (2017, unknown): “Earlier work on privacy preserving machine learning has focused on decision trees [31], k-means clustering [28], [14], SVM classification [48], [44], linear regression [19], [20], [40] and logistic regression [42].”
YerbaBuena: Securing Deep Learning Inference Data via Enclave-based Ternary Model Partitioning (2018, methodology): “SMC has been used for several fundamental machine learning tasks[10, 26, 31, 37, 50, 51].”
Membership Inference Attacks Against Machine Learning Models (2016, background): “py Bayes classifiers [33], and k-means clustering [22].”
Oblivious Neural Network Computing via Homomorphic Encryption (2007, methodology): “Solutions for the following algorithms were proposed: decision trees [6], neural networks [8], SVM [9], naive bayes classifiers [10], belief networks [11, 12], clustering [13].”
Semantic Security: Privacy Definitions Revisited (2013, methodology): “See privacy preserving SVM [26], privacy preserving DBSCAN [27], and privacy preserving k-means [39] [17] for just a few examples.”
Privacy-preserving SVM classification (2008, unknown): “Specifically, secure methods have been proposed for association rule mining [18, 39], clustering [17, 22, 35], classification [10, 36, 38, 43], outlier detection [37], and regression [21, 31].”
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 inputs.”
Secure two-party computations in ANSI C (2012, methodology): “Experimental results with the new tool CBMC-GC demonstrate the practical usefulness of our approach.”
Privacy-Preserving Collaborative Deep Learning with Irregular Participants. (2018, methodology): “Privacypreserving techniques such as secure multi-party computation or differential privacy have been applied to linear and logistic regression analysis [8], [9], k-means clustering [10], support vector machines [11], and crowd machine learning [12].”
Big data for cyber physical systems in industry 4.0: a survey (2019, background): “In such a setting, network bandwidth is the bottleneck and the critical issue is how to reduce the amount of data communication between computers(/clusters) (Jagannathan and Wright 2005).”
Privacy-Preserving Backpropagation Neural Network Learning (2009, background): “After these two papers, a good number of data mining tasks have been studied with the consideration of privacy protection, for example classification [13], clustering [14],”