Privacy-preserving data analysis refers to techniques that allow organizations to analyze data while protecting individuals' personal information. This approach ensures that sensitive data remains confidential, even when used for research or decision-making. Methods like differential privacy and secure multi-party computation help achieve this by adding noise to data or allowing computations without revealing the actual data. These techniques are essential in various fields, including healthcare and finance, where data privacy is crucial. By using privacy-preserving methods, organizations can gain valuable insights from data without compromising the privacy of individuals, fostering trust and compliance with regulations like GDPR.