The ENCRYPT project, funded under the Horizon Europe Framework, aims to advance privacy-preserving technologies across various sectors, ensuring robust data protection while maintaining utility. By providing users with core methodologies including the Fully Homomorphic Encryption, Trusted Execution Environments, Differential Privacy and advanced Hybrid Protection Services, ENCRYPT seeks to address the challenge of ensuring data privacy and utility, across federated data spaces within the EU. Differential privacy is an important approach within ENCRYPT, in protecting individual privacy in the growing landscape of digital data. In this paper, we provide an overview of fundamental concepts and present an overview of differential privacy foundations, examining its theoretical underpinnings and practical implementations. We also provide an insight into how it will be applied within the ENCRYPT project. Experiments carried out demonstrate that differential privacy can maintain high data accuracy despite the addition of noise, and we will describe how the ENCRYPT platform simplifies the use of this privacy-preserving technology for non-expert users by automating privacy parameter selection and model optimization. This approach enhances data security, efficiency and accessibility, helping to develop a more privacy-conscious environment for data analysis to carry out research and innovation in a secure and private manner. We will also explore potential future developments and applications of differential privacy within various industries and sectors.