Incremental learning of privacy-preserving Bayesian networks

Samet, Saeed; Miri, Ali; Granger, Éric

doi:10.1016/j.asoc.2013.03.011

Cited by 18 publications

(7 citation statements)

References 20 publications

(21 reference statements)

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“…Set the tree parameters C , D , and E to 1 will make the algorithm to be reduce to the K2 algorithm. For this, many researchers cite this algorithm as a generalization of the K2 algorithm [30,31,36]. At the end, a structure of alternative networks results from the set of parent sets and the network parameters, denoted by a combined Bayesian network.…”

Section: Buntine's Solutionmentioning

confidence: 99%

Continuous Learning of the Structure of Bayesian Networks: A Mapping Study

Silva¹,

Bezerra²,

Perkusich³

et al. 2019

Bayesian Networks - Advances and Novel Applications

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Bayesian networks can be built based on knowledge, data, or both. Independent of the source of information used to build the model, inaccuracies might occur or the application domain might change. Therefore, there is a need to continuously improve the model during its usage. As new data are collected, algorithms to continuously incorporate the updated knowledge can play an essential role in this process. In regard to the continuous learning of the Bayesian network's structure, the current solutions are based on its structural refinement or adaptation. Recent researchers aim to reduce complexity and memory usage, allowing to solve complex and large-scale practical problems. This study aims to identify and evaluate solutions for the continuous learning of the Bayesian network's structures, as well as to outline related future research directions. Our attention remains on the structures because the accurate parameters are completely useless if the structure is not representative.

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Section: Buntine's Solutionmentioning

confidence: 99%

Continuous Learning of the Structure of Bayesian Networks: A Mapping Study

Silva¹,

Bezerra²,

Perkusich³

et al. 2019

Bayesian Networks - Advances and Novel Applications

View full text Add to dashboard Cite

show abstract

“…So far many secure protocols have been developed for data mining and machine learning techniques such as [10][11] for d ci ion tr cl ific tion, [12][13][14] for clustering, [15], [16] for association rule mining, [17][18][19] for Neural Networks, and [20][21] for Bayesian Networks.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Data Mining of Distributed Databases Using Naïve Bayes Classifier

Ouda

Salem

2013

JES. Journal of Engineering Sciences

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Privacy-preserving data mining is discovering accurate patterns and rules without precise access to the original data. In this paper, we propose a novel algorithm for privacy preserving data mining. The proposed algorithm is based on the integration of RSA public key cryptosystem and homomorphic encryption scheme. No data is shared between distributed parties except the final result. Data mining algorithm is performed locally for each party. The final result of all parties is compared to get the target value. Previous solution for privacy preserving data mining of Naive Bayes classifier is based on secure sum that may permit collusion between parties, which is not here in proposed solution. Theoretical analysis and experimental results show that the proposed algorithm can provide good capability of privacy preserving, accuracy and efficiency.

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“…Some recent works [4,7,16,20,35,43] that rely on the storage of partial old data have made impressive progress. They are arguably not memory efficient and storing data for the life time involves violate some practical constraints such as copyright or privacy issues, which is common in the domains like bio-informatics [47]. The performance of the existing methods that do not store any past data is yet unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

Class-incremental Learning via Deep Model Consolidation

Zhang¹,

Zhang²,

Ghosh³

et al. 2019

Preprint

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Deep neural networks (DNNs) often suffer from "catastrophic forgetting" during incremental learning (IL) -an abrupt degradation of performance on the original set of classes when the training objective is adapted to a newly added set of classes. Existing IL approaches tend to produce a model that is biased towards either the old classes or new classes, unless with the help of exemplars of the old data. To address this issue, we propose a classincremental learning paradigm called Deep Model Consolidation (DMC), which works well even when the original training data is not available. The idea is to first train a separate model only for the new classes, and then combine the two individual models trained on data of two distinct set of classes (old classes and new classes) via a novel double distillation training objective. The two existing models are consolidated by exploiting publicly available unlabeled auxiliary data. This overcomes the potential difficulties due to unavailability of original training data. Compared to the state-of-the-art techniques, DMC demonstrates significantly better performance in image classification (CIFAR-100 and CUB-200) and object detection (PASCAL VOC 2007) in the single-headed IL setting.

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Incremental learning of privacy-preserving Bayesian networks

Cited by 18 publications

References 20 publications

Continuous Learning of the Structure of Bayesian Networks: A Mapping Study

Continuous Learning of the Structure of Bayesian Networks: A Mapping Study

Privacy-Preserving Data Mining of Distributed Databases Using Naïve Bayes Classifier

Class-incremental Learning via Deep Model Consolidation

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