The advancement of artificial intelligence-generated content drives the diversification of healthcare services, resulting in increased private information collection by healthcare service providers. Therefore, compliance with privacy regulations has increasingly become a paramount concern for both regulatory authorities and consumers. Privacy policies are crucial for consumers to understand how their personal information is collected, stored, and processed. In this work, we propose a privacy policy text compliance reasoning framework called FACTOR, which harnesses the power of large language models (LLMs). Since the General Data Protection Regulation (GDPR) has broad applicability, this work selects Article 13 of the GDPR as regulation requirements. FACTOR segments the privacy policy text using a sliding window strategy and employs LLM-based text entailment to assess compliance for each segment. The framework then applies a rule-based ensemble approach to aggregate the entailment results for all regulation requirements from the GDPR. Our experiments on a synthetic corpus of 388 privacy policies demonstrate the effectiveness of FACTOR. Additionally, we analyze 100 randomly selected websites offering healthcare services, revealing that nine of them lack a privacy policy altogether, while 29 have privacy policy texts that fail to meet the regulation requirements.

The diversified development of the service ecosystem, particularly the rapid growth of services like cloud and edge computing, has propelled the flourishing expansion of the service trading market. However, in the absence of appropriate pricing guidance, service providers often devise pricing strategies solely based on their own interests, potentially hindering the maximization of overall market profits. This challenge is even more severe in edge computing scenarios, as different edge service providers are dispersed across various regions and influenced by multiple factors, making it challenging to establish a unified pricing model. This paper introduces a multi-participant stochastic game model to formalize the pricing problem of multiple edge services. Subsequently, an incentive mechanism based on Pareto improvement is proposed to drive the game towards Pareto optimal direction, achieving optimal profits. Finally, an enhanced PSO algorithm was proposed by adaptively optimizing inertia factor across three stages. This optimization significantly improved the efficiency of solving the game model and analyzed equilibrium states under various evolutionary mechanisms. Experimental results demonstrate that the proposed pricing incentive mechanism promotes more effective and rational pricing allocations, while also demonstrating the effectiveness of our algorithm in resolving game problems.