An Entropy-Driven Evolutionary Game Framework for Quantifying Privacy-Service Tradeoffs

With the rapid advancement of networked applications, privacy protection has emerged as a paramount concern for billions of users and service providers worldwide. While users have grown accustomed to submitting personal information through registration forms to access digital services, and service providers depend on such data disclosures to deliver tailored experiences, there remains a significant gap in protective frameworks. Current practices lack systematic mechanisms to empower users with optimal privacy control while disclosing only the minimally necessary information required for service acquisition. This imbalance between data disclosure and privacy preservation underscores a fundamental challenge in contemporary digital ecosystems. To address this issue, this paper proposes an evolutionary game theory approach combined with information entropy to explore how to provide more accurate and personalized services for users while minimizing privacy exposure. Focusing on real-world interactions in social networks, the game is exclusively played between users and service providers, aiming to strike a balance between user privacy and service precision. The theory of entropy is employed to quantify the information disclosed by users, and a privacy calculation formula is derived using a sensitivity coefficient and a modified information entropy formula. Building on this foundation, we present the time-varying dynamic replication equation, the equilibrium solution, and the evolutionary stable strategy (ESS). To validate our approach, we collected 200 real-world questionnaire responses and conducted simulation experiments. The results demonstrate that our strategy can provide users with an optimal solution, effectively balancing privacy preservation with the need for personalized services.