Differentially Private Range Queries with Correlated Input Perturbation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915007970672640 |
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| author | Dharangutte, Prathamesh Gao, Jie Gong, Ruobin Wang, Guanyang |
| author_facet | Dharangutte, Prathamesh Gao, Jie Gong, Ruobin Wang, Guanyang |
| contents | This work proposes a class of differentially private mechanisms for linear queries, in particular range queries, that leverages correlated input perturbation to simultaneously achieve unbiasedness, consistency, statistical transparency, and control over utility requirements in terms of accuracy targets expressed either in certain query margins or as implied by the hierarchical database structure. The proposed Cascade Sampling algorithm instantiates the mechanism exactly and efficiently. Our theoretical and empirical analysis demonstrates that we achieve near-optimal utility, effectively compete with other methods, and retain all the favorable statistical properties discussed earlier. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentially Private Range Queries with Correlated Input Perturbation Dharangutte, Prathamesh Gao, Jie Gong, Ruobin Wang, Guanyang Cryptography and Security Machine Learning Methodology This work proposes a class of differentially private mechanisms for linear queries, in particular range queries, that leverages correlated input perturbation to simultaneously achieve unbiasedness, consistency, statistical transparency, and control over utility requirements in terms of accuracy targets expressed either in certain query margins or as implied by the hierarchical database structure. The proposed Cascade Sampling algorithm instantiates the mechanism exactly and efficiently. Our theoretical and empirical analysis demonstrates that we achieve near-optimal utility, effectively compete with other methods, and retain all the favorable statistical properties discussed earlier. |
| title | Differentially Private Range Queries with Correlated Input Perturbation |
| topic | Cryptography and Security Machine Learning Methodology |
| url | https://arxiv.org/abs/2402.07066 |