A practical vehicular-fog-based privacy-preserving attribute-based keyword search system using potential games

The growth of fog computing drives Internet of Things users to outsource data for services like storage and sharing. To ensure secure and efficient data search, extensive research has been conducted on searchable encryption, specifically attribute-based keyword search (ABKS). ABKS facilitates precise access control over encrypted data while maintaining search functionality. However, delegating complex search operations to fog nodes introduces challenges like latency limitations, compromised search results, and unauthorized occupation of server resources. In this manuscript, we propose a Time-Efficient Data Retrieval (TEDR) scheme to address these challenges. Our scheme incorporates two access policies, allowing data owners (DOs) and data users (DUs) to control data access and search queries. TEDR enables fog nodes to verify search queries before decryption, avoiding unnecessary decryption for invalid queries. We introduce a decentralized query submitting framework to allocate search queries among fog nodes, addressing redundancy concerns and latency issues. We analyze the decentralized query submitting game and classify it as a potential game, making it suitable for decentralized decision-making. We evaluate TEDR performance on real-world datasets, demonstrating practicality and efficiency. Our scheme reduces computational and communication costs by approximately 30% compared to existing methods. Our TEDR scheme proposes a novel approach to secure and efficient data search in fog computing environments. It utilizes game theory to address challenges in delegating search operations to fog nodes for outsourced data services. Incorporating two access policies and a decentralized query-submitting framework, TEDR ensures secure and efficient data search with reduced computational and communication costs image