Privacy-Preserving Fully Distributed Gaussian Process Regression
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915656354496512 |
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| author | Jang, Yeongjun Teranishi, Kaoru Suh, Jihoon Tanaka, Takashi |
| author_facet | Jang, Yeongjun Teranishi, Kaoru Suh, Jihoon Tanaka, Takashi |
| contents | Although distributed Gaussian process regression (GPR) enables multiple agents with separate datasets to jointly learn a model of the target function, its collaborative nature poses risks of private data leakage. To address this, we propose a privacy-preserving fully distributed GPR protocol based on secure multi-party computation (SMPC) that preserves the confidentiality of each agent's local dataset. Building upon a secure distributed average consensus algorithm, the protocol guarantees that each agent's local model practically converges to the same global model that would be obtained by the standard distributed GPR. Further, we adopt the paradigm of simulation based security to provide formal privacy guarantees, and extend the proposed protocol to enable kernel hyperparameter optimization, which is critical yet often overlooked in the literature. Experimental results demonstrate the effectiveness and practical applicability of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Fully Distributed Gaussian Process Regression Jang, Yeongjun Teranishi, Kaoru Suh, Jihoon Tanaka, Takashi Systems and Control Although distributed Gaussian process regression (GPR) enables multiple agents with separate datasets to jointly learn a model of the target function, its collaborative nature poses risks of private data leakage. To address this, we propose a privacy-preserving fully distributed GPR protocol based on secure multi-party computation (SMPC) that preserves the confidentiality of each agent's local dataset. Building upon a secure distributed average consensus algorithm, the protocol guarantees that each agent's local model practically converges to the same global model that would be obtained by the standard distributed GPR. Further, we adopt the paradigm of simulation based security to provide formal privacy guarantees, and extend the proposed protocol to enable kernel hyperparameter optimization, which is critical yet often overlooked in the literature. Experimental results demonstrate the effectiveness and practical applicability of the proposed method. |
| title | Privacy-Preserving Fully Distributed Gaussian Process Regression |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.05473 |