Privacy-enhancing technologies (PETs) are technologies that embody the fundamental principles of data protection by minimizing personal data use, maximizing data security, and enhancing individual agency. PETs protect the privacy of personal information of users authorized by services or applications. They employ techniques to minimize the possession of personal data by information systems without losing functionality. However, there is no unified definition of PETs to quantify privacy since the objectives and scenarios depend on practical applications. In this lecture, we start from the motivation for privacy, illustrate why PETs are necessary through real-life events, and then introduce an overview of existing privacy solution technologies, including federated learning, secure multi-party computation, homomorphic encryption, differential privacy, and zero-knowledge proofs, among others.