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Federated CPU Tracker

Modern organizations often need to monitor CPU usage across their network to optimize performance and resource allocation. However, this presents a significant security challenge: raw CPU data can be exploited by sophisticated attacks like Hertzbleed and PLATYPUS to extract sensitive information, including cryptographic keys.

To address this challenge, we've developed a solution that uses Differential privacy - a mathematical framework that protects sensitive data by adding carefully calibrated noise. This approach strikes an optimal balance: it prevents malicious actors from accessing exact CPU measurements while preserving the statistical utility of the aggregated data for legitimate monitoring purposes.

Using SyftBox's API framework, we'll build a system that:

  • Collects CPU usage data securely
  • Applies differential privacy protection automatically
  • Enables safe sharing of protected metrics across the SyftBox network