Abstract:

With the growing adoption of cloud computing, data owners are increasingly outsourcing their data to cloud servers for convenience and cost efficiency. However, ensuring data privacy requires encrypting sensitive information before outsourcing, which complicates traditional data retrieval methods such as keyword-based searches. To address this challenge, we propose a secure multi-keyword ranked search scheme over encrypted cloud data that also supports dynamic update operations, including document insertion and deletion. Our approach integrates the vector space model and the widely used TF-IDF model in index construction and query generation. We design a specialized tree-based index structure and introduce a Greedy Depth-first Search algorithm to enhance search efficiency. To ensure both security and accurate relevance ranking, we employ the secure kNN algorithm for encrypting index and query vectors. Additionally, phantom terms are incorporated into the index vector to counteract statistical attacks and prevent

Description:

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