Privacy-Preserving Cram'er-Rao Lower Bound
The paper develops a privacy-preserving Cramér-Rao Lower Bound (CRLB) theory to find the fundamental limit of identification accuracy when data is stochastically obfuscated. It establishes a bound that quantifies the trade-off between achieving an accurate parameter estimate and maintaining privacy, providing a rigorous benchmark for system identification algorithms.