👉 Privacy math is a branch of cryptography that focuses on quantifying and ensuring privacy in computations involving sensitive data. It involves mathematical techniques that allow for the secure processing of information without revealing the underlying data itself. By using advanced cryptographic methods, such as homomorphic encryption and secure multi-party computation, privacy math enables computations on encrypted data, ensuring that the data remains private throughout the process. This means that while useful computations can be performed, the actual data remains confidential, even from those who have access to the computation environment. Essentially, privacy math provides a framework for performing operations on private data in a way that guarantees individual privacy and data protection.
