Privacy-preserving Naïve Bayes classification

By Jaideep Vaidya, Murat Kantarcioglu, Chris Clifton. 2008

238 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

The Value of Collaboration in Convex Machine Learning with Differential Privacy (2019, methodology): “In the past, secure multi-party computation has been used in various ML models, such as decision trees [5], regression [6], association rules [7], and clustering [8],”
Blockchain-Based Privacy Preserving Deep Learning (2018, methodology): “1) Techniques based on Secure Multi-party Computation (SMC) can help protect intermediate steps of the computation, such as decision trees [13], linear regression functions [14], association rules [15], Naive Bayes classifiers [16], and k-means clustering [17],when multiple parties perform collabora”
Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers (2010, background): “The techniques typically provide guarantees that none of the parties learn anything about the individual data besides what may be inferred from the final result of the computation.”
Privacy-Preserving Machine Learning for Speech Processing (2012, unknown): “…has been a lot of work on constructing privacy preserving algorithms for specific problems in privacy 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,…”
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].”
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].”
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization (2018, background): “Lindell and Pinkas [31] proposed one of the earliest approaches to use MPC for private data mining, which was followed by several works considering different adversarial models or applications [55, 50, 33, 42].”
When Machine Learning Meets Privacy (2020, methodology): “SMC has been used for a variety of traditional ML models, including decision trees [7], linear regression [30, 66, 107, 135, 136], logistic regression [143, 170], Naive Bayes classifiers [157], and k-means clustering [16, 62].”
Machine Learning Classification over Encrypted Data (2015, methodology): “We show an example of a full analysis in Section VI for the Naïve Bayes classifier.”
DP-ADMM: ADMM-Based Distributed Learning With Differential Privacy (2018, background): “Another stateof-art approach on private computation is secure multiparty computation [22], [23], by which each party can only learn the computation results and the personal data is kept private, but this approach is very computational expensive and barely used in practical systems.”
Membership Inference Attacks Against Machine Learning Models (2016, background): “py Bayes classifiers [33], and k-means clustering [22].”
Privacy-preserving outsourced classification in cloud computing (2017, background): “A lot of works have been supposed for privacy-preserving training algorithm, such as back-propagation networks [16, 24], naïve bayes [25,26], K-means clustering [27–29], association rules [30,31], linear regression [32,33], decision trees [31,34] and so forth.”
Chiron: Privacy-preserving Machine Learning as a Service (2018, background): “Many papers describe how to use cryptographic techniques, including secure multiparty computation, to learn and apply relatively simple classifiers without decrypting the data [25, 27, 28, 47, 63].”