Effective Deep Learning Technique for Enhanced Data Privacy and Security

Abstract: In the past, data privacy and security during analysis were challenging. Sensitive information often remained vulnerable, risking privacy breaches. This research introduces a comprehensive solution to address these challenges. It consists of three main stages: PII detection, differential privacy with Gaussian noise, and homomorphic encryption. It starts with data collection from various sources. What sets is the system apart is its ability to safeguard personal data. This research employ PII detection tech- niques to identify and anonymize sensitive information, preserving privacy without compromising data utility. Next, preprocess the data, enhancing its quality for analysis. Differential privacy is applied, intro- ducing controlled Gaussian noise and aggregating the data to protect individual privacy while enabling meaningful insights. Moreover, This research uses homomorphic encryption, which allows confidential calculations to be performed without revealing sensitive information. This is especially beneficial for securing indian household data. As move on to data analysis, the research system leverages machine learning and analytical methods to extract insights from the protected data. Finally, the results are visu- alized and presented in reports, ensuring that the protected data is effectively utilized while respecting privacy and security concerns. In summary, the system provides a comprehensive solution for handling sensitive data, ensuring privacy, and enabling valuable insights to be drawn from the data without com- promising individuals privacy and data security. It significantly enhances data privacy and security compared to the past, where these concerns were inadequately addressed.