Privacy-Preserving Data Analysis: Perform data Analysis on Encrypted Data Stored in the Cloud
Abstract
Balancing data security with analytical performance in the analysis of encrypted data has proven to be a challenging task. An initiative called Privacy-Preserving Data Analysis uses Python to conduct safe data analysis on cloud-stored encrypted data. The purpose of this project is to reduce the possibility that private data may be discovered when analyzing the data. Many methods are used to do this, including secure multiparty computation and homomorphic encryption. The project entails putting in place a safe data processing pipeline that protects the confidentiality of the data that is stored. With homomorphic encryption, sensitive data can be computed directly on encrypted data, eliminating the need for decryption and revealing it. In this project, the Python programming language serves as the primary development tool. It's a great option because of its widespread libraries, community support, and ease of use. The project entails utilizing Python to create privacy-preserving protocols and encryption techniques and to integrate them with cloud storage providers. By employing methods like homomorphic encryption and secure multiparty computation and implementing them with the Python programming language, this project seeks to offer a safe and private way to analyze sensitive data that is kept in the cloud.
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