TokenizedDB: Text Tokenization using NLP for Enhanced Storage Efficiency and Data Privacy

SQL databases are widely utilized for data storage across various domains. However, storing text values in SQL databases requires a considerable amount of storage, even if numerous parts of the text are redundant. Traditional compression methods, while helpful, are limited in optimizing storage within databases. Tokenizers used for natural language processing (NLP) can convert text to tokens. This paper proposes a method called TokenizedDB to utilize tokenization to store text as tokens. This approach leads to benefits such as a reduction of storage space. Storage of text as tokens leads to increased data privacy in the event of database breaches since the attacker would be unaware of the text being stored as tokens and the method used for the tokenization process. The attackers fail to understand the method of text storage and fail to reconstruct the original text without being aware of the methods used for the tokenization. The system can be implemented in addition to the existing encryption methods to ensure data privacy. The results display up to 66% reduction in text storage requirements in some cases. The system proved its potential to enhance storage space efficiency to reduce storage costs while strengthening security protocols for text. The code is available at github.com/Pro-GenAI/TokenizedDB. Keywords: natural language processing (NLP), information retrieval, SQL databases, data storage optimization, data privacy, data security, storage efficiency