Density Peak Clustering Algorithm Based on Differential Privacy Preserving

Chen, Yun; Du, Yunlan; Cao, Xiaomei

doi:10.1007/978-3-030-34637-9_2

Cited by 6 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, aiming at the subjectivity of cluster center selection and the problems existing in the slope change trend of the ranking graph defined in [18] and [27], the weight (i−1)/i is introduced to redefine the trend of slope change of ranking graph creatively, and the threshold value of ranking graph statistics γ is obtained to realize automatic clustering center selection. Finally, aiming at the problem of privacy leakage, according to literature [31], the Laplacian noise of appropriate privacy budget is added to the core statistic of the algorithm (local density). In the whole process of the algorithm, as a distance measurement method, the calculation of local density and center offset distance of cosine distance is the same as the method using Euclidean distance, so the mechanism of adding Laplacian noise to local density is also consistent with that using Euclidean distance.…”

Section: Improvement Of Dpc Algorithm Based On Differential Privacymentioning

confidence: 99%

“…Chen proposed DP-CFSFDP by combining DPC algorithm with differential privacy technology. In order to solve the problem that noisy parameters may lead to deviation between the new center point and the correct center point, reachable center point is introduced to DP-RCCFSFDP [31]. Sun et al proposed DP-DPCSNNS based on shared nearest neighbor similarity [32], which used shared nearest neighbor similarity to calculate local density and detect cluster centers with neighborhood information, thus improving the accuracy of cluster center selection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

View full text Add to dashboard Cite

A new density peak clustering (DPC) algorithm with adaptive clustering center based on differential privacy was proposed to solve the problems of poor adaptability of high-dimensional data, inability to automatically determine clustering centers, and privacy problems in clustering analysis. First, to solve the problem of poor adaptability of high-dimensional data, cosine distance was used to measure the similarity between high-dimensional datasets. Then, aiming at the subjective problem of clustering center selection, from the perspective of ranking graph, the weight (i − 1)/i was introduced creatively, the slope trend of ranking graph was redefined to realize the adaptive clustering center. Finally, aiming at the privacy problem, the Laplacian noise of appropriate privacy budget was added to the core statistic (local density) of the algorithm to achieve the balance between privacy protection and algorithm effectiveness. Experimental results on both the synthetic and UCI datasets show that this algorithm can not only realize the automatic selection of clustering center, but also solve the privacy problem in clustering analysis, and improve the clustering evaluation index greatly, which proves the effectiveness of the algorithm.INDEX TERMS Cosine distance, differential privacy, DPC algorithm, Laplacian noise, trend of slope change.

show abstract

Section: Improvement Of Dpc Algorithm Based On Differential Privacymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…After that, Hu [28] proposed the DPk-means-up algorithm, which improved the clustering effect compared with the former scholars under the same level of privacy protection. In 2015, Wu [29] proposed the DP-DBScan algorithm by combining density clustering the DBScan algorithm with differential privacy protection technology. In 2018, Wang [30] proposed an improved the DP-OPTICS differential privacy protection algorithm and verified the feasibility of this method.…”

Section: B Clustering Algorithm With Differential Privacy Preservingmentioning

confidence: 99%

Improved Denclue Outlier Detection Algorithm With Differential Privacy and Attribute Fuzzy Priority Relation Ordering

Xia,

Chen,

Wang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Outlier detection is an important method in data mining. Although Denclue algorithm is particularly good at finding clusters of arbitrary shape and detecting outliers, it does not protect the user's privacy well in the operation process. In this paper, differential privacy technology is introduced into Denclue algorithm to ensure the privacy security in the application of Denclue algorithm and outlier detection. Firstly, the differential privacy technology is used to add the Laplacian noise to the density to realize the sensitive information hiding between data objects. Secondly, in order to compensate for the decrease of outlier detection accuracy caused by noise, the information entropy weight distance was introduced to amplify the influence of important attributes in the algorithm, and the density function of entropy weight distance was used to calculate each data point. Finally, through the method of ordering fuzzy priority relation, a new measure index is defined by analogy to measure the degree of outliers between attributes. According to the measure index, the attributes are reordered and the weight distance of information entropy is improved. A differential privacy the Denclue outlier detection algorithm based on attribute fuzzy priority ordering (EAF-DP-Denclue) is proposed. The numerical results of the experiment show that the performance of EAF-DP-Denclue is more than that of traditional algorithms, and the identification process of EAF-DP-Denclue protects sensitive privacy information, and is better than that of the DP-DBScan outlier detection algorithm.

show abstract

“…In [42], the differentially private k-means algorithm and k-modes algorithm were combined to cluster mixed data in a privacy-preserving manner, and the allocation of privacy budgets was optimised. In [43], a density peak clustering algorithm based on differential privacy named DP-CFSFDP was proposed, along with its improved version named DP-rcCFSFDP for better performance on data with uniform distribution. [44] studied the problem of noninteractive clustering in the distributed setting under the framework of local DP, and developed kCluster algorithm that was suitable for clustering in the anonymised space.…”

Section: Differentially Private Clustering Algorithmsmentioning

confidence: 99%

Privacy‐preserving constrained spectral clustering algorithm for large‐scale data sets

Wei

et al. 2020

IET Information Security

View full text Add to dashboard Cite

Density Peak Clustering Algorithm Based on Differential Privacy Preserving

Cited by 6 publications

References 22 publications

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

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

Improved Denclue Outlier Detection Algorithm With Differential Privacy and Attribute Fuzzy Priority Relation Ordering

Privacy‐preserving constrained spectral clustering algorithm for large‐scale data sets

Contact Info

Product

Resources

About