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原文摘要

In spectrum coexistence systems, the primary user (PU) activity must be partially disclosed for interference avoidance and coordination, while the exact spectrum occupancy information is often restricted due to security and regulatory constraints. To this end, subset-based disclosures can be employed in which the true frequency is included while ambiguity is introduced. This paper shows that naive randomized subset disclosures remain highly vulnerable to information leakage through inference, as temporal structure can be exploited to recover spectrum usage information. Under Markovian dynamics of the PU's spectrum usage, hidden Markov model (HMM) filtering with online learning is applied, while under periodic dynamics, period and phase estimations with phase-aligned statistics are used for inference. To address this vulnerability, structure-aware obfuscation strategies are introduced. For Markovian dynamics, history-based and belief-based schemes are designed to induce model mismatch and limit posterior concentration, while for periodic dynamics, cycle-dominance and decoy-based schemes are designed to manipulate phase-wise statistics and bias the inference process. These obfuscation schemes significantly hinder inference of spectrum information and offer principled disclosure solutions for spectrum coexistence systems.