Privacy-preserving SVM classification

By Jaideep Vaidya, Hwanjo Yu, Xiaoqian Jiang. 2008

194 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “[5] are early examples of work that sought to learn from local data using a centralized server while preserving privacy.”
Federated Tensor Factorization for Computational Phenotyping (2017, background): “Electronic health records (EHRs) become one of most important sources of information about patients, which provide insight into diagnoses [19] and prognoses [11], as well as assist in the development of cost-effective treatment and management programs [1, 12].”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “[82] propose a method to train an SVMmodel where the training data is distributed among multiple parties.”
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].”
L G ] 1 1 A pr 2 01 7 Federated Tensor Factorization for Computational Phenotyping (2018, background): “Many privacy preserving data mining algorithms aim at constructing a global model only from aggregated statistics locally generated by participating institutions on their own data, without seeing others’ data at a fine-grained level [22, 27].”
Privacy-Preserving Machine Learning for Speech Processing (2012, background): “…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,…”
ABY3: A Mixed Protocol Framework for Machine Learning (2018, methodology): “Earlier work on privacy preserving machine learning considered decision trees [39], k-means clustering [15, 35], SVM classification [56, 58], linear regression [25, 26, 49] and logistic regression [52].”
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.”