privacy-aware-networked-control

name: privacy-aware-networked-control description: Privacy-aware co-design of quantizer and controller in networked control systems. Solves stochastic control problems with mutual information regularization to prevent privacy leakage. Use for secure networked control, privacy-preserving IoT systems, and adversarial-resilient control design.

Privacy-Aware Co-Design of Quantizer and Controller

This skill implements optimal privacy-aware networked control through joint design of quantizer and controller, protecting private system inputs from adversarial inference.

Overview

The framework addresses privacy concerns in networked control systems where measurements are sent to remote controllers after stochastic quantization. An adversary attempts to infer private system inputs from quantization results and control outputs.

Key Features:

• Mutual information-based privacy leakage measurement
• Coupled Bellman equations for optimal quantizer/controller
• Closed-loop belief regulation for enhanced privacy
• Policy gradient optimization with binary classification

When to Use This Skill

• Networked control with privacy-sensitive inputs
• Remote control systems with quantized measurements
• IoT systems requiring data privacy
• Adversarial environments with eavesdropping threats

Problem Formulation

System Model

• Dynamical System: Affected by private input process
• Quantizer: Stochastic quantization before transmission
• Controller: Remote controller using quantized measurements
• Adversary: Seeks to infer private inputs from observations

Privacy Measure

Mutual information quantifies privacy leakage:

I(Private Inputs; Quantization Results, Control Outputs)

Mathematical Framework

Coupled Bellman Equations

Dynamic programming decomposition yields coupled equations for:

• Optimal Quantizer: Regulates adversary's belief
• Optimal Controller: Deterministic control law

Structural Properties

Optimization Approach

1. Joint Parameterization: Quantizer and controller jointly parameterized
2. Policy Gradient: Update via policy gradient methods
3. Privacy Approximation: Binary classification for leakage estimation

Implementation Guide

Algorithm Steps

1. Initialize quantizer and controller parameters
2. Observe system state and private inputs
3. Apply stochastic quantization
4. Transmit quantized measurement
5. Compute control action
6. Update parameters via policy gradient
7. Estimate privacy leakage using binary classifier

Design Considerations

• Quantization levels trade off privacy vs. control performance
• Mutual information regularization strength affects privacy-utility balance
• Policy gradient step size impacts convergence

Validation

Numerical experiments demonstrate effectiveness on:

• Building control systems
• HVAC systems with occupancy privacy
• Smart grid with consumption privacy

References

Paper: Optimal Privacy-Aware Co-Design of Quantizer and Controller in Networked Control Systems

• Authors: Chuanghong Weng, Ehsan Nekouei
• arXiv: 2604.08860
• Date: 2026-04-10
• Categories: eess.SY

Related Concepts

• Differential privacy in control systems
• Secure networked control
• Information-theoretic privacy
• Stochastic quantization

Activation Keywords

• privacy-aware-networked-control
• privacy aware networked
• privacy aware networked control

Tools Used

• read - 读取技能文档
• write - 创建输出
• exec - 执行相关命令

Instructions for Agents

1. 理解技能的核心方法论
2. 根据用户问题提供针对性回答
3. 遵循最佳实践

Examples

Example 1: 基本查询

User: 请解释 Privacy Aware Networked Control

Agent: Privacy Aware Networked Control 是关于...