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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08233 |
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| _version_ | 1866915314412814336 |
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| author | Chen, Ding Liu, Chen |
| author_facet | Chen, Ding Liu, Chen |
| contents | We investigate the differential privacy (DP) guarantees under the hidden state assumption (HSA) for multi-convex problems. Recent analyses of privacy loss under the hidden state assumption have relied on strong assumptions such as convexity, thereby limiting their applicability to practical problems. In this paper, we introduce the Differential Privacy Mini-Batch Block Coordinate Descent (DP-MBCD) algorithm, accompanied by the privacy loss accounting methods under the hidden state assumption. Our proposed methods apply to a broad range of classical non-convex problems which are or can be converted to multi-convex problems, such as matrix factorization and neural network training. In addition to a tighter bound for privacy loss, our theoretical analysis is also compatible with proximal gradient descent and adaptive calibrated noise scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08233 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hidden State Differential Private Mini-Batch Block Coordinate Descent for Multi-convexity Optimization Chen, Ding Liu, Chen Machine Learning We investigate the differential privacy (DP) guarantees under the hidden state assumption (HSA) for multi-convex problems. Recent analyses of privacy loss under the hidden state assumption have relied on strong assumptions such as convexity, thereby limiting their applicability to practical problems. In this paper, we introduce the Differential Privacy Mini-Batch Block Coordinate Descent (DP-MBCD) algorithm, accompanied by the privacy loss accounting methods under the hidden state assumption. Our proposed methods apply to a broad range of classical non-convex problems which are or can be converted to multi-convex problems, such as matrix factorization and neural network training. In addition to a tighter bound for privacy loss, our theoretical analysis is also compatible with proximal gradient descent and adaptive calibrated noise scenarios. |
| title | Hidden State Differential Private Mini-Batch Block Coordinate Descent for Multi-convexity Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.08233 |