Privacy-Preserving Data Fragmentation and Aggregation

Privacy-preserving data fragmentation and aggregation techniques aim to preserve the privacy of individual data points while still allowing for the aggregation of data for analysis. This is important for applications such as medical research, where it is necessary to share data without compromising the privacy of the patients. This paper surveys the state-of-the-art in privacy-preserving data fragmentation and aggregation techniques. We discuss the different challenges that need to be addressed in this area, and we present a number of different techniques that have been proposed. We also discuss the security and privacy guarantees of these techniques, and we evaluate their performance.