PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917286471794688 |
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| author | Obiefuna, Nnaemeka Oyeneye, Samuel Odunaiya, Similoluwa Oyelaja, Iremide Kolawole, Steven |
| author_facet | Obiefuna, Nnaemeka Oyeneye, Samuel Odunaiya, Similoluwa Oyelaja, Iremide Kolawole, Steven |
| contents | Privacy preserving machine learning deployments in sensitive deep learning applications; from medical imaging to autonomous systems; increasingly require combining multiple techniques. Yet, practitioners lack systematic guidance to assess the synergistic and non-additive interactions of these hybrid configurations, relying instead on isolated technique analysis that misses critical system level interactions. We introduce PrivacyBench, a benchmarking framework that reveals striking failures in privacy technique combinations with severe deployment implications. Through systematic evaluation across ResNet18 and ViT models on medical datasets, we uncover that FL + DP combinations exhibit severe convergence failure, with accuracy dropping from 98% to 13% while compute costs and energy consumption substantially increase. In contrast, FL + SMPC maintains near-baseline performance with modest overhead. Our framework provides the first systematic platform for evaluating privacy-utility-cost trade-offs through automated YAML configuration, resource monitoring, and reproducible experimental protocols. PrivacyBench enables practitioners to identify problematic technique interactions before deployment, moving privacy-preserving computer vision from ad-hoc evaluation toward principled systems design. These findings demonstrate that privacy techniques cannot be composed arbitrarily and provide critical guidance for robust deployment in resource-constrained environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_18900 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems Obiefuna, Nnaemeka Oyeneye, Samuel Odunaiya, Similoluwa Oyelaja, Iremide Kolawole, Steven Cryptography and Security Computer Vision and Pattern Recognition I.4.9; C.2.4; K.6.5 Privacy preserving machine learning deployments in sensitive deep learning applications; from medical imaging to autonomous systems; increasingly require combining multiple techniques. Yet, practitioners lack systematic guidance to assess the synergistic and non-additive interactions of these hybrid configurations, relying instead on isolated technique analysis that misses critical system level interactions. We introduce PrivacyBench, a benchmarking framework that reveals striking failures in privacy technique combinations with severe deployment implications. Through systematic evaluation across ResNet18 and ViT models on medical datasets, we uncover that FL + DP combinations exhibit severe convergence failure, with accuracy dropping from 98% to 13% while compute costs and energy consumption substantially increase. In contrast, FL + SMPC maintains near-baseline performance with modest overhead. Our framework provides the first systematic platform for evaluating privacy-utility-cost trade-offs through automated YAML configuration, resource monitoring, and reproducible experimental protocols. PrivacyBench enables practitioners to identify problematic technique interactions before deployment, moving privacy-preserving computer vision from ad-hoc evaluation toward principled systems design. These findings demonstrate that privacy techniques cannot be composed arbitrarily and provide critical guidance for robust deployment in resource-constrained environments. |
| title | PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems |
| topic | Cryptography and Security Computer Vision and Pattern Recognition I.4.9; C.2.4; K.6.5 |
| url | https://arxiv.org/abs/2602.18900 |