Networked System Detection of Stealthy Data Attacks with Privacy Noise
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Solution Overview
Problem
Existing privacy protection schemes for networked systems are not integrated with attack detection schemes, leading to increased stealthiness of data tampering by attackers, and there is a lack of comprehensive security to protect against data stealthy attacks that undermine system stability.
Innovation Solution
A method is developed to model networked systems, design an attack detection scheme based on system noise parameters, and determine moments for adding privacy noise through a scheduling scheme to enhance detection while ensuring privacy, using differential privacy protection to optimize system control performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a privacy protection scheme is deployed to protect sensitive data, then privacy security is improved, but the system introduces uncertainties that increase attacker stealthiness and reduce detection effectiveness
Solution Approach 1:
The patent applies preliminary action by pre-calculating the impact of privacy protection schemes on system noise characteristics before deployment. The detection scheme is designed in advance with knowledge of how differential privacy adds noise to measurements, allowing the anomaly detection thresholds and parameters to be pre-adjusted to account for this noise. This enables the system to maintain effective attack detection despite the presence of privacy-protecting noise, as the detection scheme was prepared in advance knowing exactly what noise patterns to expect.
2Reliability
If differential privacy noise is added to protect sensitive data, then privacy protection is improved, but system control performance deteriorates due to increased noise impact
Solution Approach 1:
The patent applies partial action by selectively adding differential privacy noise only to specific measurements or data elements that require privacy protection, rather than adding noise to all system measurements uniformly. The privacy noise scheduling scheme determines which measurements receive privacy protection based on their sensitivity and importance to control performance. This partial application of privacy noise reduces the overall impact on system control performance while still providing necessary privacy protection for sensitive data.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the differential privacy parameters (such as noise scale and privacy budget ε) based on system operating conditions and sensitivity requirements. The privacy noise scheduling scheme modifies noise injection parameters adaptively, reducing noise intensity when control performance is critical and increasing it when privacy protection is prioritized. This dynamic parameter adjustment allows the system to balance between privacy protection and control performance across different operational scenarios.
Data Source
AI summary
Disclosed is a method for detecting a data stealthy attack on a networked system with differential privacy protection, which falls within the technical field of information security. The method includes: firstly, modeling a networked system and designing an attack detection scheme based on system noise parameters; then, designing an optimal data stealthy attack scheme for an attacker according to known system information; and then, determining a moment of adding a privacy noise through a privacy noise scheduling scheme while ensuring privacy of sensitive data on the networked system, and achieving an optimal system control performance. Based on the above design, a possible data stealthy attack may be effectively detected on the basis of protecting the system data privacy and ensuring the optimal system performance.