Privacy preserving distributed machine learning with federated learning

Chamikara, Mahawaga Arachchige Pathum; Bertók, Péter; Khalil, Ibrahim; Liu, Dongxi; Camtepe, Seyit

doi:10.1016/j.comcom.2021.02.014

Cited by 92 publications

(48 citation statements)

References 49 publications

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“…This segregated collection contributes to anonymizing data because it disassociates confidential attributes from original quasi-identifiers. Unlike previous protocols that output k-anonymized data, Chamikara et al [35] presents a perturbative mashup protocol that provides noisy anonymized data to train distributed machine learning models. Data perturbation is caused by geometric data transformations, randomized expansion noise addition, and data shuffling.…”

Section: Privacy-preserving Data Mashupmentioning

confidence: 99%

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

2021

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Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students’ learning. However, data sets involved in mashups may contain personal information of sensitive nature that raises legitimate privacy concerns. Average privacy preservation methods are based on preemptive approaches that limit the published data in a mashup based on access control and authentication schemes. Such limitations may reduce the analytical utility of the data exposed to gain students’ learning insights. In order to reconcile utility and privacy preservation of published data, this research proposes a new data mashup protocol capable of merging and k-anonymizing data sets in cloud-based learning environments without jeopardizing the analytical utility of the information. The implementation of the protocol is based on linked data so that data sets involved in the mashups are semantically described, thereby enabling their combination with relevant educational data sources. The k-anonymized data sets returned by the protocol still retain essential information for supporting general data exploration and statistical analysis tasks. The analytical and empirical evaluation shows that the proposed protocol prevents individuals’ sensitive information from re-identifying.

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Section: Privacy-preserving Data Mashupmentioning

confidence: 99%

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

2021

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“…Additive perturbation-based PPFL methods. Additive perturbation-based FL methods aim to preserve privacy by adding random noise to weight updates or gradient updates [19,52,63,69,70,78,110,172,193,198]. In some methods [52,78,193], random noise was added to weight updates to achieve privacy-preserving in the training process, whereas in other methods [69,70,172,198], random noise was added to the gradient updates.…”

Section: Encryption-based Ppflmentioning

confidence: 99%

A Comprehensive Survey of Privacy-preserving Federated Learning

2021

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The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

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“…In order to adjust the characteristics and the number of trees, w is the weight value and L is the original loss function. In the original distributed ML, joint modeling is realized by sending F(x) to participants, but distributed ML can use F(x) to calculate data labels backward, resulting in data leakage, which does not meet the basic requirements of FL in principle [27]. e federated tree model is based on the Secure Boost [26] encryption algorithm, training the samples of the model that needs joint training, and the first sample and the second sample are trained to get the prediction model of the decision tree.…”

Section: Federated Decision Treementioning

confidence: 99%

Federated Learning: A Distributed Shared Machine Learning Method

Xia

et al. 2021

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Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machine learning and deep learning. First of all, this paper introduces the development process, definition, architecture, and classification of FL and explains the concept of FL by comparing it with traditional distributed learning. Then, it describes typical problems of FL that need to be solved. On the basis of classical FL algorithms, several federated machine learning algorithms are briefly introduced, with emphasis on deep learning and classification and comparisons of those algorithms are carried out. Finally, this paper discusses possible future developments of FL based on deep learning.

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Privacy preserving distributed machine learning with federated learning

Cited by 92 publications

References 49 publications

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

A Comprehensive Survey of Privacy-preserving Federated Learning

Federated Learning: A Distributed Shared Machine Learning Method

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