FBF: A High-Efficient Query Mechanism for Keyword Search in Online Social Networks

Abstract

The widespread adoption of online social networks has facilitated content sharing among users. However, privacy controls restrict users' access to only a limited portion of the network, typically limited to direct connections or two-hop friends. Browsing relevant profiles and home pages has become a common practice for users, but the vast amount of data involved often hampers their ability to efficiently retrieve the desired information. This paper presents an efficient keyword search model designed to aid users in accessing the required information effectively. Leveraging advancements in Bloom filter technology, we propose a novel summary index called Friend-based Bloom filter (FBF), which enables large-scale full-text retrieval while reducing inter-server communication costs and query latency. We conduct a comprehensive simulation to evaluate our ranking model, and the results demonstrate the effectiveness of the FBF scheme. Specifically, our approach achieves a reduction of 92.4% in inter-server communication costs and 78.7% in query latency, with a high-search precision condition resulting in a remarkable 98.3% improvement.