Fusion of Differential Privacy Algorithm and Advanced AI: Innovating Customer Information Protection in Transaction Data

In the digital age, where data-driven insights are pivotal for business decisions and customer privacy concerns are mounting rapidly, safeguarding sensitive transaction data, such as invoices, has become a paramount priority. The proliferation of stringent data protection regulations like General Data Protection Rule (GDPR) further accentuates this need, mandating businesses to adopt robust privacy-preserving mechanisms. This paper advocates the use of differential privacy, specifically the Laplace mechanism, as an innovative solution to protect customer information in modern commercial transactions. By strategically introducing Laplace noise to sensitive attributes of transaction data, we achieve ε-differential privacy, which mathematically constrains the impact of any individual record on the output, thereby mitigating the risk of unauthorized disclosure. This approach not only conforms to the evolving privacy standards but also preserves the analytical value of aggregate data, enabling businesses to leverage big data while respecting individual privacy rights. Furthermore, as businesses navigate the intricate terrain of data protection and analytics, the application of differential privacy in transaction data processing holds immense promise. It offers a feasible pathway for companies to maintain customer trust, comply with regulations, and excel in a data-driven economy. Prospectively, future advancements in this technology may integrate sophisticated machine learning and AI techniques to further refine the privacy-utility trade-offs, positioning differential privacy as an indispensable tool for securing customer information in the future. As the digital economy continues to evolve, differential privacy stands as a beacon of hope for businesses striving to balance the need for data analytics with the imperative of protecting customer privacy.