Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866913317648334848 |
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| author | Shao, Jiaqi Han, Shanshan He, Chaoyang Luo, Bing |
| author_facet | Shao, Jiaqi Han, Shanshan He, Chaoyang Luo, Bing |
| contents | Federated heavy-hitter analytics involves the identification of the most frequent items within distributed data. Existing methods for this task often encounter challenges such as compromising privacy or sacrificing utility. To address these issues, we introduce a novel privacy-preserving algorithm that exploits the hierarchical structure to discover local and global heavy hitters in non-IID data by utilizing perturbation and similarity techniques. We conduct extensive evaluations on both synthetic and real datasets to validate the effectiveness of our approach. We also present FedCampus, a demonstration application to showcase the capabilities of our algorithm in analyzing population statistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_02277 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data Shao, Jiaqi Han, Shanshan He, Chaoyang Luo, Bing Distributed, Parallel, and Cluster Computing Federated heavy-hitter analytics involves the identification of the most frequent items within distributed data. Existing methods for this task often encounter challenges such as compromising privacy or sacrificing utility. To address these issues, we introduce a novel privacy-preserving algorithm that exploits the hierarchical structure to discover local and global heavy hitters in non-IID data by utilizing perturbation and similarity techniques. We conduct extensive evaluations on both synthetic and real datasets to validate the effectiveness of our approach. We also present FedCampus, a demonstration application to showcase the capabilities of our algorithm in analyzing population statistics. |
| title | Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2307.02277 |