Homomorphic Encryption and Secure Multi-Party Computation: Mathematical Tools for Privacy-Preserving Data Analysis in the Cloud

Abstract

With more and more people using cloud computing and storing and handling data remotely, protecting the privacy and safety of private data has become very important. Homomorphic encryption and safe multi-party computing (MPC) are two new mathematics tools that offer strong ways to analyze data in the cloud while protecting privacy. When you use homomorphic encryption, you can do calculations directly on protected data, so you can process data securely without having to decode private data. This method makes sure that data stays protected while operations are being done, keeping it safe from people who shouldn't have access to it or seeing it. Cloud service providers can use homomorphic encryption to do different types of analysis on protected data, like collecting, searching, and machine learning, without revealing the private information that lies beneath. Secure multi-party computation protects privacy in situations where multiple people work together to analyze data. MPC allows for joint analysis without letting other people see individual datasets by spreading computations across multiple entities, each of which holds a piece of the data. MPC uses cryptographic protocols and methods to make sure that processes are done without revealing private inputs. This lets multiple people work together to analyze data while keeping privacy. These math tools can be used for many different types of data analysis jobs in the cloud, such as predictive modeling, machine learning, and statistical analysis. They also make it safe for different groups to share and work together on data, like businesses, academics, and people, without putting data protection at risk. There are still problems with how homomorphic encryption and safe MPC can be used in the real world and how they can be scaled up. These problems are mostly related to the amount of work that needs to be done and how efficiently it works. The main goal of ongoing study is to create improved methods and programs that will make these techniques work better and be easier to use in the real world.