A New Density Peak Clustering Algorithm With Adaptive Clustering Center Based on Differential Privacy

Chen, Hua; Zhou, Yuanming; Mei, Kehui; Wang, Nan; Cai, Guangxing

doi:10.1109/access.2022.3233196

Cited by 9 publications

(18 citation statements)

References 33 publications

(46 reference statements)

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“…From the experiments performed and the results obtained (Tables 3 and 4), it is revealed that the ARI obtained on the given data sets is better than other approaches [15, 18, 28–30]. The other observed phenomenon is that whenever clustering is performed using the SOM, the most optimum 2D configuration would be preferable by considering the ST or CH score to obtain optimized results because the WM of the SOM is also very meaningful.…”

Section: Conclusion and Future Scopementioning

confidence: 93%

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

Tripathi

2024

Health Care Science

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IntroductionData clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.MethodsIn this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.ResultsThe adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.ConclusionsIt is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

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Section: Conclusion and Future Scopementioning

confidence: 93%

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

Tripathi

2024

Health Care Science

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“…In recent years, the leakage of private information of patients has occurred frequently. With the advancement of cloud technology and big data, it is easier for attackers to collect patients' private information and speculate about patients' sensitive information through correlation and other means [32]. Therefore, while combining machine learning models with cancer diagnosis, it is also necessary to pay attention to the privacy protection of data.…”

Section: Machine Learning Methods Have Contributed To the Early Diagn...mentioning

confidence: 99%

Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model

Chen,

Wang,

Zhou

et al. 2023

Applied Sciences

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In order to improve the classification effect of the logistic regression (LR) model for breast cancer prediction, a new hybrid feature selection method is proposed to process the data, using the Pearson correlation test and the iterative random forest algorithm based on out-of-bag estimation (RF-OOB) to screen the optimal 17 features as inputs to the model. Secondly, the LR is optimized using the batch gradient descent (BGD-LR) algorithm to train the loss function of the model to minimize the loss. In order to protect the privacy of breast cancer patients, a differential privacy protection technology is added to the BGD-LR model, and an LR optimization model based on differential privacy with batch gradient descent (BDP-LR) is constructed. Finally, experiments are carried out on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Meanwhile, accuracy, precision, recall, and F1-score are selected as the four main evaluation indicators. Moreover, the hyperparameters of each model are determined by the grid search method and the cross-validation method. The experimental results show that after hybrid feature selection, the optimal results of the four main evaluation indicators of the BGD-LR model are 0.9912, 1, 0.9886, and 0.9943, in which the accuracy, recall, and F1-scores are increased by 2.63%, 3.41%, and 1.76%, respectively. For the BDP-LR model, when the privacy budget ε is taken as 0.8, the classification performance and privacy protection effect of the model reach an effective balance. At the same time, the four main evaluation indicators of the model are 0.9721, 0.9975, 0.9664, and 0.9816, which are improved by 1.58%, 0.26%, 1.81%, and 1.07%, respectively. Comparative analysis shows that the models of BGD-LR and BDP-LR constructed in this paper perform better than other classification models.

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“…To address concerns regarding the LDA model and the potential exposure of textual information in the training process, Zhao et al 16 proposed several differential privacy LDA algorithms tailored to typical training scenarios. Chen et al 17 introduces a differential privacy-preserving density peak clustering algorithm to address privacy protection concerns in data mining. Han et al 18 proposes a cluster-based hierarchical federated learning framework with both differential privacy and secure aggregation.…”

Section: Differential Privacy In Centralized Settingmentioning

confidence: 99%

LoHDP: Adaptive local differential privacy for high‐dimensional data publishing

Shen,

Cai,

Huang

et al. 2024

Concurrency and Computation

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SummaryThe increasing availability of high‐dimensional data collected from numerous users has led to the need for multi‐dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters. These models are then used to derive low‐dimensional marginals of these clusters, allowing for an approximation of the distribution of the original dataset and the generation of synthetic datasets. Existing solutions have limitations in computing the marginals of pairwise attributes and multi‐dimensional distribution on attribute clusters, as well as constructing relational dependency graphs that contain large clusters. To address these problems, we propose LoHDP, a high‐dimensional data publishing method composed of adaptive marginal computing and an effective attribute clustering method. The adaptive local marginal calculates any k‐dimensional marginals required in the algorithm. In particular, methods such as sampling‐based randomized response are used instead of privacy budget splits to perturb user data. The attribute clustering method measures the correlation between pairwise attributes using an effective method, reduces the search space during the construction of the dependency graph using high‐pass filtering technology, and realizes dimensionality reduction by combining sufficient triangulation operation. We demonstrate through extensive experiments on real datasets that our LoHDP method outperforms existing methods in terms of synthetic dataset quality.

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A New Density Peak Clustering Algorithm With Adaptive Clustering Center Based on Differential Privacy

Cited by 9 publications

References 33 publications

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model

LoHDP: Adaptive local differential privacy for high‐dimensional data publishing

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