Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data

Aminifar, Amin; Shokri, Matin; Rabbi, Fazle; Pun, Ka I; Lamo, Yngve

doi:10.1109/access.2022.3141709

Cited by 28 publications

(12 citation statements)

References 139 publications

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“…The extra-tree classifier uses a meta estimator that fits a number of randomized decision trees (i.e., extra trees) on various subsamples of the dataset and employs averaging to improve the predictive accuracy and control overfitting [71] . This classifier has several parameters, e.g., criterion, maximum of features ( max_features ), minimum of the sample leaf ( min_sample_leaf ), and minimum of the sample split ( min_sample_split ).…”

Section: Resultsmentioning

confidence: 99%

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Hidayat

Julian²,

Dharmawan

et al. 2022

Artificial Intelligence in Medicine

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Section: Resultsmentioning

confidence: 99%

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Hidayat

Julian²,

Dharmawan

et al. 2022

Artificial Intelligence in Medicine

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“…It is crucial to assess the collective model with high accuracy to use the correct amount of randomness and the right approach. The notion of privacy in characterizing randomness analyze in the conventional privacy architecture, disclosure risk, and destruction metrics in data handling [11]; however, it describes in current designs [12].…”

Section: Related Workmentioning

confidence: 99%

“…Delete existing data and replacing it with new data is a logical technique to arbitrarily a collection of elements. Paper [11] looks into the choose-a-size group of arbitrariness algorithms. A choose-a-size arbitrariness operator is constructed for a fixed record size |r| = n and has three conditions: a arbitrariness level 0< ρ < 1 and a distribution function (d [0], d [1],.., d[n]) over the dataset {0, 1, .…”

Section: Dataset Arbitrarinessmentioning

confidence: 99%

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Murugeshwari¹,

Rajalakshmi²,

Sudharson³

2023

Computer Systems Science and Engineering

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Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results. Furthermore, this paper investigates the use of arbitrary nature with perturbations in privacy preservation. According to the research, arbitrary objects (most notably random matrices) have "predicted" frequency patterns. It shows how to recover crucial information from a sample damaged by a random number using an arbitrary lattice spectral selection strategy. This filtration system's conceptual framework posits, and extensive practical findings indicate that sparse data distortions preserve relatively modest privacy protection in various situations. As a result, the research framework is efficient and effective in maintaining data privacy and security.

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“…It is the most accurate and computationally efficient algorithm. 55 It is different from the other tree-ensemble algorithms based on two reasons: first is that it divides the nodes by selecting the cut points fully at random basis and second one is that it takes the whole training data set (no subsets of the data set) to create more trees. Recently, ET ensemble is selected as the proposed scheme for ETD in SGs in Gunturi and Sarkar.…”

Section: Extra Treesmentioning

confidence: 99%

Electricity theft detection for energy optimization using deep learning models

Pamir,

Javaid,

Javed

et al. 2023

Energy Science & Engineering

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The rapid increase in nontechnical loss (NTL) has become a principal concern for distribution system operators (DSOs) over the years. Electricity theft makes up a major part of NTL. It causes losses for the DSOs and also deteriorates the quality of electricity. The introduction of advanced metering infrastructure along with the upgradation of the traditional grids to the smart grids (SGs) has helped the electric utilities to collect the electricity consumption (EC) readings of consumers, which further empowers the machine learning (ML) algorithms to be exploited for efficient electricity theft detection (ETD). However, there are still some shortcomings, such as class imbalance, curse of dimensionality, and bypassing the automated tuning of hyperparameters in the existing ML‐based theft classification schemes that limit their performances. Therefore, it is essential to develop a novel approach to deal with these problems and efficiently detect electricity theft in SGs. Using the salp swarm algorithm (SSA), gate convolutional autoencoder (GCAE), and cost‐sensitive learning and long short‐term memory (CSLSTM), an effective ETD model named SSA–GCAE–CSLSTM is proposed in this work. Furthermore, a hybrid GCAE model is developed via the combination of gated recurrent unit and convolutional autoencoder. The proposed model comprises five submodules: (1) data preparation, (2) data balancing, (3) dimensionality reduction, (4) hyperparameters' optimization, and (5) electricity theft classification. The real‐time EC data provided by the state grid corporation of China are used for performance evaluations via extensive simulations. The proposed model is compared with two basic models, CSLSTM and GCAE–CSLSTM, along with seven benchmarks, support vector machine, decision tree, extra trees, random forest, adaptive boosting, extreme gradient boosting, and convolutional neural network. The results exhibit that SSA–GCAE–CSLSTM yields 99.45% precision, 95.93% F1 score, 92.25% accuracy, and 71.13% area under the receiver operating characteristic curve score, and surpasses the other models in terms of ETD.

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Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data

Cited by 28 publications

References 139 publications

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Electricity theft detection for energy optimization using deep learning models

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