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The recommendation idea is inspired by the user’s search habits and experience, there is a demand that the server should provide active recommendation. However, data encryption makes keywords lose their semantics, which hinders the analysis and recommendation function of the server. In this paper, we investigate searchable encryption by focusing on its “search pattern” and “access pattern,” and propose a novel ciphertext search model that supports association analysis and recommendation. Our scheme design adheres to the fundamental security assumptions of searchable encryption, permitting the disclosure of only these two patterns. Specifically, the “search pattern” refers to metadata about query behavior (e.g., repeated searches), while the “access pattern” consists of the encrypted keywords associated with files. The core association analysis and active recommendation mechanism operates entirely under these security constraints, it functions by treating uploaded keyword ciphertexts as tags, allowing the server to construct an association graph based on their relationships, this enables effective data analysis without compromising user confidentiality. The proposed framework follows a clear workflow: a Data Owner encrypts their documents and generates an index; subsequently, a user performs a search, which triggers our secure association and recommendation mechanism. This process balances robust security with acceptable performance. We detail the construction of this foundational Searchable Encryption (SE) framework, which integrates access control, association analysis, and recommendation methods. Finally, extensive security and performance analyses are conducted. To demonstrate the feasibility of ERecommend, we analyze its time and space complexity, providing a comprehensive benchmark from perspectives including encryption, decryption, and search efficiency. Experimental outcomes indicate that ERecommend not only ensures strong security but also delivers efficient performance, showcasing its practical potential.
Published in: Journal of Cloud Computing Advances Systems and Applications