Design of Privacy Preservation Model for Data Stream Using Condensation Based Anonymization

Abstract

In modern world, continuous data flows that may be analyzed in real-time and are created from multiple sources are referred to as data streams. As data streams become increasingly prevalent across various sectors, preserving the privacy of sensitive information while maintaining data utility is a significant challenge. Due to privacy concerns in most of the organizations, data are shared with third party or the public. In many cases, users are hesitant to provide personal information. This paper proposes a privacy-preservation model for data streams based on condensation-based anonymization. This model uses a condensation mechanism to reduce the granularity of data while ensuring that personally identifiable information and sensitive attributes are effectively anonymized. By leveraging a condensation approach, the model selectively compresses data, allowing for both privacy protection and the retention of essential data patterns for analysis. The condensation method minimizes lost information throughout the anonymization process in an effort to maintain the statistical features of the data. Using generalization and suppression approaches, k-Anonymity obscures identifying information in datasets, making it a useful tool for protecting individual privacy. The proposed model employs hybrid of K- Anonymity algorithm and condensation method to enhance the security and privacy of the data.