| _version_ | 1867003918385414144 |
|---|---|
| author | Guohua Shen Mengnan Cai Zhiqiu Huang Yang Yang Feifei Guo Linlin Wei |
| author_facet | Guohua Shen Mengnan Cai Zhiqiu Huang Yang Yang Feifei Guo Linlin Wei Guohua Shen Mengnan Cai Zhiqiu Huang Yang Yang Feifei Guo Linlin Wei |
| collection | Wiley Open Access |
| contents | LoHDP: Adaptive local differential privacy for high‐dimensional data publishing Guohua Shen Mengnan Cai Zhiqiu Huang Yang Yang Feifei Guo Linlin Wei Concurrency and Computation: Practice and Experience 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 anyk‐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. 10.1002/cpe.8039 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/cpe.8039 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_cpe_8039 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | LoHDP: Adaptive local differential privacy for high‐dimensional data publishing Guohua Shen Mengnan Cai Zhiqiu Huang Yang Yang Feifei Guo Linlin Wei Concurrency and Computation: Practice and Experience LoHDP: Adaptive local differential privacy for high‐dimensional data publishing Guohua Shen Mengnan Cai Zhiqiu Huang Yang Yang Feifei Guo Linlin Wei Concurrency and Computation: Practice and Experience 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 anyk‐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. 10.1002/cpe.8039 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | LoHDP: Adaptive local differential privacy for high‐dimensional data publishing |
| topic | Concurrency and Computation: Practice and Experience |
| url | https://onlinelibrary.wiley.com/doi/10.1002/cpe.8039 |