Privacy labels (e.g., Data Safety section on Google Play) aim to replace lengthy privacy policies with concise and standardized summaries of in-app privacy practices. However, studies reveal widespread inaccuracies in these self-reported labels, with developers omitting or misrepresenting privacy practices, undermining user trust and regulatory compliance. Existing methods for detecting such discrepancies lack coverage or scalability and fail to address the semantic ambiguity inherent in privacy label auditing. We present Iterative Context Reconstruction (ICR), an evidence-driven workflow that reconstructs context from decompiled code to resolve the ambiguity. Based on ICR, PriLabel: Towards Comprehensively Uncovering Omitted Disclosures in Privacy Labels of Android Apps on a Large Scale is a context-aware static auditor that comprehensively uncoversomitted disclosuresin Android privacy labels, mapping transmitted data to Google’s label taxonomy in asource-freeandontology-freemanner. Our evaluation demonstrates PriLabel: Towards Comprehensively Uncovering Omitted Disclosures in Privacy Labels of Android Apps on a Large Scale’s high precision (91.5%) in detecting omitted disclosures in privacy labels. Applied to 4,851 top-installed Google Play apps, it revealed that 2,374 apps omitted at least one disclosure, with 210 transmitting sensitive financial data (e.g., credit card numbers) without proper labeling, exposing systemic risks of non-compliance.

Channel fading can have a strong impact on the convergence of over-the-air federated edge learning (OTA-FEEL). This paper develops a new and optimal power control policy to minimize the optimality gap of OTA-FEEL under any independently and identically distributed fading. Specifically, we reveal that the optimal power control policy takes a structure where the variance of the effective channel from a device to the server should be minimized when its mean is given. Following this structure, a novel nested optimization algorithm is developed to iteratively minimize the variance using the Lagrange-dual method and then optimize the mean of the effective channel using one-dimensional search. A quasi-closed-form expression of the optimal power control policy is derived. It is shown that the optimal adaptive power control for OTA-FEEL performs an integration of the “channel inverting” strategy and the opposite “channel-proportional” strategy to balance the mean and variance of the effective channel. We also generalize the new policy when the channel statistics are unknown a-priori, and show that the optimal policy can be asymptotically approached over time. Simulations confirm the superiority of the policy to its existing alternatives in the convergence speed and learning accuracy of OTA-FEEL.