Internet users rely on mobile and Web applications (apps) to provide services that meet a variety of everyday needs. These services increasingly rely on access to personal data, either collected directly from users or accessed through third parties or data brokers. The growing access to personal information, coupled with the scale of app development in the market, presents significant concerns for protecting users’ privacy. To mitigate these concerns, it is crucial for software developers to identify and understand privacy requirements early in the development process. In this chapter, we focus on acquisition methods and analysis of privacy requirements. We first define privacy requirements and introduce various sources containing such requirements. We then review state-of-the-art methods, including Natural Language Processing (NLP) techniques for extracting privacy requirements and their challenges in acquiring requirements from various sources. Finally, we show traceability and verification of privacy requirements in practice. This chapter demonstrates the significant contribution of acquisition methods in enhancing the accuracy and efficiency of extraction and analysis of privacy requirements.