$(ε, δ)$-Differentially Private Partial Least Squares Regression
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916519890386944 |
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| author | Nikzad-Langerodi, Ramin Kumar, Mohit Duy, Du Nguyen Alghasi, Mahtab |
| author_facet | Nikzad-Langerodi, Ramin Kumar, Mohit Duy, Du Nguyen Alghasi, Mahtab |
| contents | As data-privacy requirements are becoming increasingly stringent and statistical models based on sensitive data are being deployed and used more routinely, protecting data-privacy becomes pivotal. Partial Least Squares (PLS) regression is the premier tool for building such models in analytical chemistry, yet it does not inherently provide privacy guarantees, leaving sensitive (training) data vulnerable to privacy attacks. To address this gap, we propose an $(ε, δ)$-differentially private PLS (edPLS) algorithm, which integrates well-studied and theoretically motivated Gaussian noise-adding mechanisms into the PLS algorithm to ensure the privacy of the data underlying the model. Our approach involves adding carefully calibrated Gaussian noise to the outputs of four key functions in the PLS algorithm: the weights, scores, $X$-loadings, and $Y$-loadings. The noise variance is determined based on the global sensitivity of each function, ensuring that the privacy loss is controlled according to the $(ε, δ)$-differential privacy framework. Specifically, we derive the sensitivity bounds for each function and use these bounds to calibrate the noise added to the model components. Experimental results demonstrate that edPLS effectively renders privacy attacks, aimed at recovering unique sources of variability in the training data, ineffective. Application of edPLS to the NIR corn benchmark dataset shows that the root mean squared error of prediction (RMSEP) remains competitive even at strong privacy levels (i.e., $ε=1$), given proper pre-processing of the corresponding spectra. These findings highlight the practical utility of edPLS in creating privacy-preserving multivariate calibrations and for the analysis of their privacy-utility trade-offs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09164 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | $(ε, δ)$-Differentially Private Partial Least Squares Regression Nikzad-Langerodi, Ramin Kumar, Mohit Duy, Du Nguyen Alghasi, Mahtab Machine Learning As data-privacy requirements are becoming increasingly stringent and statistical models based on sensitive data are being deployed and used more routinely, protecting data-privacy becomes pivotal. Partial Least Squares (PLS) regression is the premier tool for building such models in analytical chemistry, yet it does not inherently provide privacy guarantees, leaving sensitive (training) data vulnerable to privacy attacks. To address this gap, we propose an $(ε, δ)$-differentially private PLS (edPLS) algorithm, which integrates well-studied and theoretically motivated Gaussian noise-adding mechanisms into the PLS algorithm to ensure the privacy of the data underlying the model. Our approach involves adding carefully calibrated Gaussian noise to the outputs of four key functions in the PLS algorithm: the weights, scores, $X$-loadings, and $Y$-loadings. The noise variance is determined based on the global sensitivity of each function, ensuring that the privacy loss is controlled according to the $(ε, δ)$-differential privacy framework. Specifically, we derive the sensitivity bounds for each function and use these bounds to calibrate the noise added to the model components. Experimental results demonstrate that edPLS effectively renders privacy attacks, aimed at recovering unique sources of variability in the training data, ineffective. Application of edPLS to the NIR corn benchmark dataset shows that the root mean squared error of prediction (RMSEP) remains competitive even at strong privacy levels (i.e., $ε=1$), given proper pre-processing of the corresponding spectra. These findings highlight the practical utility of edPLS in creating privacy-preserving multivariate calibrations and for the analysis of their privacy-utility trade-offs. |
| title | $(ε, δ)$-Differentially Private Partial Least Squares Regression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.09164 |