K-Means Clustering with Local Distance Privacy
With the development of information technology, a mass of data are generated every day. Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition. K-means clustering has been widely used for cluster analysis in real life....
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2023-12-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020050 |
| _version_ | 1797384795825635328 |
|---|---|
| author | Mengmeng Yang Longxia Huang Chenghua Tang |
| author_facet | Mengmeng Yang Longxia Huang Chenghua Tang |
| author_sort | Mengmeng Yang |
| collection | DOAJ |
| description | With the development of information technology, a mass of data are generated every day. Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition. K-means clustering has been widely used for cluster analysis in real life. However, these analyses are based on users’ data, which disclose users’ privacy. Local differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering analysis. However, existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy guarantee. To solve this problem, we propose a novel method that provides local distance privacy for users who participate in the clustering analysis. Instead of making the users’ records in-distinguish from each other in high-dimensional space, we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each other. To be specific, we generate a noisy distance first and then synthesize the high-dimensional data record. We propose a Bounded Laplace Method (BLM) and a Cluster Indistinguishable Method (CIM) to sample such a noisy distance, which satisfies the local differential privacy guarantee and local dE-privacy guarantee, respectively. Furthermore, we introduce a way to generate synthetic data records in high-dimensional space. Our experimental evaluation results show that our methods outperform the traditional methods significantly. |
| first_indexed | 2024-03-08T21:44:48Z |
| format | Article |
| id | doaj.art-33b0b8276f4346eaba69c440509189ac |
| institution | Directory Open Access Journal |
| issn | 2096-0654 |
| language | English |
| last_indexed | 2024-03-08T21:44:48Z |
| publishDate | 2023-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj.art-33b0b8276f4346eaba69c440509189ac2023-12-20T09:32:32ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-12-016443344210.26599/BDMA.2022.9020050K-Means Clustering with Local Distance PrivacyMengmeng Yang0Longxia Huang1Chenghua Tang2Data61, Commonwealth Scientific and Industrial Research Organization, Melbourne 3168, AustraliaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541010, ChinaWith the development of information technology, a mass of data are generated every day. Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition. K-means clustering has been widely used for cluster analysis in real life. However, these analyses are based on users’ data, which disclose users’ privacy. Local differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering analysis. However, existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy guarantee. To solve this problem, we propose a novel method that provides local distance privacy for users who participate in the clustering analysis. Instead of making the users’ records in-distinguish from each other in high-dimensional space, we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each other. To be specific, we generate a noisy distance first and then synthesize the high-dimensional data record. We propose a Bounded Laplace Method (BLM) and a Cluster Indistinguishable Method (CIM) to sample such a noisy distance, which satisfies the local differential privacy guarantee and local dE-privacy guarantee, respectively. Furthermore, we introduce a way to generate synthetic data records in high-dimensional space. Our experimental evaluation results show that our methods outperform the traditional methods significantly.https://www.sciopen.com/article/10.26599/BDMA.2022.9020050k-means clusteringlocal differential privacydata analysis |
| spellingShingle | Mengmeng Yang Longxia Huang Chenghua Tang K-Means Clustering with Local Distance Privacy Big Data Mining and Analytics k-means clustering local differential privacy data analysis |
| title | K-Means Clustering with Local Distance Privacy |
| title_full | K-Means Clustering with Local Distance Privacy |
| title_fullStr | K-Means Clustering with Local Distance Privacy |
| title_full_unstemmed | K-Means Clustering with Local Distance Privacy |
| title_short | K-Means Clustering with Local Distance Privacy |
| title_sort | k means clustering with local distance privacy |
| topic | k-means clustering local differential privacy data analysis |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020050 |
| work_keys_str_mv | AT mengmengyang kmeansclusteringwithlocaldistanceprivacy AT longxiahuang kmeansclusteringwithlocaldistanceprivacy AT chenghuatang kmeansclusteringwithlocaldistanceprivacy |