Privacy-preserving computation

Privacy-preserving computation refers to techniques that allow data to be analyzed or processed without revealing the actual information to anyone involved. Think of it as performing calculations on encrypted data or using methods that isolate sensitive details, so privacy is maintained even as insights are gained. This approach enables organizations to collaborate, share insights, or use data for improvements without risking personal or confidential information. It’s essential in fields like healthcare, finance, and research, balancing the need for data utility with the obligation to protect individual privacy.