Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

Yan, Yan; Sun, Zichao; Mahmood, Adnan; Xu, Fei; Dong, Zhuoyue; Sheng, Quan Z.

doi:10.3390/ijgi11070404

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…The grid analysis method in clustering analysis can partition the data into grids. The STING (Statistical Information Grid) method is commonly used to divide the spatial region of input objects into rectangular cells [25][26][27]. Spatial division can be achieved through hierarchical and recursive methods.…”

Section: The Coordinate Grid Methodsmentioning

confidence: 99%

A Novel Optimization Method for the Drilling Process in Panel Furniture Production

Wang,

Xiong

2024

Preprint

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The panel furniture industry is experiencing rapid development, with a growing focus on intelligent manufacturing and efficient production. However, in the process of upgrading the drilling process for panel furniture towards intelligentization, still lacks standard optimization methods. To address this issue, we conducted an analysis of the cabinet structure in panel furniture and propose a novel approach that utilizes matrices and sets to represent panel and drilling information, enabling efficient data analysis and subsequent optimization. Moreover, we introduce a comprehensive optimization method comprising the COING (a hierarchical clustering analysis method adapted for panel furniture based on the STING grid method) and AR (Association Rule) techniques, uncovering potential associations and relationships within datasets. To validate the efficacy of the proposed method, we implemented it in the production workshop of Company W. Experimental results demonstrate a 14.01% reduction in drilling frequency and a 3.869% improvement in drilling efficiency compared to traditional design methods. Our findings emphasize the practical significance of the proposed standard drilling optimization method in the panel furniture industry, effectively enhancing drilling efficiency and promoting the transition to intelligent manufacturing.

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Section: The Coordinate Grid Methodsmentioning

confidence: 99%

A Novel Optimization Method for the Drilling Process in Panel Furniture Production

Wang,

Xiong

2024

Preprint

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“…Han et al 18 proposes a cluster‐based hierarchical federated learning framework with both differential privacy and secure aggregation. Yan et al 19 introduces a grid clustering and differential privacy protection method for location‐based statistical big data release scenarios, presenting a bottom‐up grid clustering algorithm.…”

Section: Related Workmentioning

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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“…Recently, some new grid-based clustering methods have been proposed to solve the above problems [ 19 , 20 , 21 , 22 , 23 , 24 ]. These methods captured attention with the advantage over other approaches because they process data with grid cells.…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, existing grid clustering algorithms work by dealing with nodes and cells. The former mainly includes FDGB [ 21 ] and GCBD [ 22 ], while the latter mainly includes GBCN [ 23 ], GCDPP [ 24 ], NGCGAL [ 25 ], and CMSPGD [ 26 ]. However, different grid-based clustering methods have their own considerations in grid space, node or cell processing, and cluster generation strategies, resulting in differences in clustering performance.…”

Section: Related Workmentioning

confidence: 99%

A Novel Clustering Method Based on Adjacent Grids Searching

Li,

Zhong,

Liao

et al. 2023

Entropy

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Clustering is used to analyze the intrinsic structure of a dataset based on the similarity of datapoints. Its widespread use, from image segmentation to object recognition and information retrieval, requires great robustness in the clustering process. In this paper, a novel clustering method based on adjacent grid searching (CAGS) is proposed. The CAGS consists of two steps: a strategy based on adaptive grid-space construction and a clustering strategy based on adjacent grid searching. In the first step, a multidimensional grid space is constructed to provide a quantization structure of the input dataset. The noise and cluster halo are automatically distinguished according to grid density. Moreover, the adaptive grid generating process solves the common problem of grid clustering, in which the number of cells increases sharply with the dimension. In the second step, a two-stage traversal process is conducted to accomplish the cluster recognition. The cluster cores with arbitrary shapes can be found by concealing the halo points. As a result, the number of clusters will be easily identified by CAGS. Therefore, CAGS has the potential to be widely used for clustering datasets with different characteristics. We test the clustering performance of CAGS through six different types of datasets: dataset with noise, large-scale dataset, high-dimensional dataset, dataset with arbitrary shapes, dataset with large differences in density between classes, and dataset with high overlap between classes. Experimental results show that CAGS, which performed best on 10 out of 11 tests, outperforms the state-of-the-art clustering methods in all the above datasets.

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Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

Cited by 9 publications

References 27 publications

A Novel Optimization Method for the Drilling Process in Panel Furniture Production

A Novel Optimization Method for the Drilling Process in Panel Furniture Production

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

A Novel Clustering Method Based on Adjacent Grids Searching

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