Build Feature Vector for String Supporting Pattern Matching on Encrypted Character Data

With the explosion of data, the demand for computing on big data is increasing, leading to companies and organizations having to hire companies with strong computing capabilities. This raises the question of how to ensure the safety of the data. In this research, we focus on addressing the problem of direct pattern matching on encrypted data in an outsourced database without the need for decryption. By using the positional encoding technique of characters in the plaintext into a binary vector, which refers to be as the Feature Vector of String \(\overrightarrow{FVS}\) , we have been able to create an encrypted search index for storage on the outsourced database. This index is to replace the plaintext or cipher text using traditional encryption algorithms. Apart from supporting high-speed pattern matching and avoiding redundant data retrieval, the vector \(\overrightarrow{FVS}\) also ensures security and prevents leakage of plaintext string information against common types of attacks.