On the Interplay of Privacy, Persuasion and Quantization
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909641449930752 |
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| author | Anand, Anju Akyol, Emrah |
| author_facet | Anand, Anju Akyol, Emrah |
| contents | We develop a communication-theoretic framework for privacy-aware and resilient decision making in cyber-physical systems under misaligned objectives between the encoder and the decoder. The encoder observes two correlated signals ($X$,$θ$) and transmits a finite-rate message $Z$ to aid a legitimate controller (the decoder) in estimating $X+θ$, while an eavesdropper intercepts $Z$ to infer the private parameter $θ$. Unlike conventional setups where encoder and decoder share a common MSE objective, here the encoder minimizes a Lagrangian that balances legitimate control fidelity and the privacy leakage about $θ$. In contrast, the decoder's goal is purely to minimize its own estimation error without regard for privacy. We analyze fully, partially, and non-revealing strategies that arise from this conflict, and characterize optimal linear encoders when the rate constraints are lifted. For finite-rate channels, we employ gradient-based methods to compute the optimal controllers. Numerical experiments illustrate how tuning the privacy parameter shapes the trade-off between control performance and resilience against unauthorized inferences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Interplay of Privacy, Persuasion and Quantization Anand, Anju Akyol, Emrah Signal Processing Computer Science and Game Theory Information Theory Systems and Control We develop a communication-theoretic framework for privacy-aware and resilient decision making in cyber-physical systems under misaligned objectives between the encoder and the decoder. The encoder observes two correlated signals ($X$,$θ$) and transmits a finite-rate message $Z$ to aid a legitimate controller (the decoder) in estimating $X+θ$, while an eavesdropper intercepts $Z$ to infer the private parameter $θ$. Unlike conventional setups where encoder and decoder share a common MSE objective, here the encoder minimizes a Lagrangian that balances legitimate control fidelity and the privacy leakage about $θ$. In contrast, the decoder's goal is purely to minimize its own estimation error without regard for privacy. We analyze fully, partially, and non-revealing strategies that arise from this conflict, and characterize optimal linear encoders when the rate constraints are lifted. For finite-rate channels, we employ gradient-based methods to compute the optimal controllers. Numerical experiments illustrate how tuning the privacy parameter shapes the trade-off between control performance and resilience against unauthorized inferences. |
| title | On the Interplay of Privacy, Persuasion and Quantization |
| topic | Signal Processing Computer Science and Game Theory Information Theory Systems and Control |
| url | https://arxiv.org/abs/2506.06321 |