Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Cloud Data

YEAR : 2022


Cloud computing has become a common strategy for financial savings and management of private information over the past year. Before outsourcing to cloud servers, however, the delicate information must be encrypted for privacy consideration, making some traditional information usage features, such as plaintext keyword search, impossible. To fix this issue, we present a search system ranked multi-keyword over encrypted cloud information that effectively supports dynamic activities. To obtain a multi-keyword ranked search, our system uses the vector space model coupled with TF IDF rule and cosine similarity measure. Traditional alternatives, however, have to bear heavy computational expenses. Our system presents Bloom filter to construct a search index tree to accomplish the sub-linear search time. Moreover, our system can correctly and efficiently promote dynamic operation due to the Bloom filter property, which implies our systems updating price is smaller than other schemes. First, we present our fundamental system, which under the known model of ciphertext is safe. Then the improved system is subsequently submitted even under the known background model to ensure safety. Real-world information set tests demonstrate that our suggested schemes ‘ performance is satisfactory.



System : Intel i3 and above
Hard Disk : 40GB
RAM : Minimum 4GB
Processer : 64-bit, four-core, 2.5 GHz minimum per core


Front End Language : HTML, CSS, JAVA, JSP SERVELTS
Backend : My SQL
Operating System : Windows 10 or 11


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