Privacy-preserving protocols are techniques designed to protect sensitive information while still allowing data to be processed and analyzed. These protocols ensure that personal data remains confidential, even when shared among multiple parties. They are essential in fields like finance, healthcare, and social media, where data privacy is crucial. One common example of a privacy-preserving protocol is differential privacy, which adds noise to datasets to prevent the identification of individuals. Another approach is secure multi-party computation, where multiple parties can compute a function over their inputs without revealing those inputs to each other. These methods help maintain privacy in an increasingly data-driven world.