PRIVACY-PRESERVING SMART NOMINATING EXPLORE BASED ON CONCEPTUAL GRAPHS OVER CIPHERED OUTSOURCED RECORDS

Explorable cipher is an important research area in cloud computing. However, most obtainable proficient and dependable Ciphertext explore schemes are based on keywords or shallow nominating parsing, which are not smart enough to meet with users' explore intention. Therefore, in this paper, we propose a content-aware explore scheme, which can make nominated explore more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to dif erent scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked explore over cipher cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacypreserving smart nominated explore based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient