DNA Computing-Enabled Big Data Anonymization Framework with a Comparative Study through Existing Anonymization Tools

Data anonymization is the process of transforming data so it protects the privacy of the individuals in the data. This is important for protecting the privacy of research participants, patients, and other individuals whose data is collected and used. There exists a number of anonymization tools in market. Though, these tools are not sure to be scalable to large datasets or may not offer the level of security essential for some requests or may not provide a flexible space for storage and retrieval. This paper presents a new big data anonymization framework that uses genetic operators for encryption and DNA computing for storage. The proposed framework is designed to be scalable to large datasets and to offer a high intensity of security. The paper also presents a comparative study of the proposed framework with existing anonymization tools. The study shows that the proposed framework is more scalable and secure than existing anonymization tools. The paper accomplishes that the proposed framework is a promising new approach to big data anonymization. The framework has the potential to be used in a various application, such as research, healthcare, and finance.