Secure and Scalable Data Processing in AWS: An Architecture for Privacy-Preserving Data Upload to Amazon Marketing Cloud
Rahul Menon · International Journal of Artificial Intelligence Data Science and Machine Learning · 2022
In the era of big data and cloud computing, ensuring the security and privacy of data is paramount, especially in sensitive domains such as marketing. Amazon Web Services (AWS) provides a robust platform for data processing and storage, but the challenge lies in designing architectures that not only scale efficiently but also protect user data. This paper presents a comprehensive architecture for secure and scalable data processing in AWS, specifically tailored for privacy-preserving data upload to Amazon Marketing Cloud (AMC). The proposed architecture leverages advanced cryptographic techniques, secure data transfer protocols, and AWS services to ensure data integrity, confidentiality, and compliance with regulatory standards. We also present a detailed algorithm for data encryption and decryption, along with performance benchmarks and a case study to validate the effectiveness of the proposed solution