Privacy-preserving machine learning is essential for keeping sensitive data secure while still allowing useful computation on encrypted or distributed datasets. Common methods such as Fully Homomorphic Encryption, Differential Privacy, and Secure Multi-party Computation provide strong privacy protection, but they often come with heavy computation costs and limited scalability. This chapter introduces a new way to improve privacy-preserving machine learning by using Approximate Computing, a hardware-based method that creates small errors which can take the place of software-added noise functions such as Gaussian or Laplacian noise. Instead of adding noise through algorithms, this approach relies on hardware-driven noise to support privacy in encryption and differential privacy. Approximate computing is a growing hardware design style that improves efficiency by allowing slight inaccuracies in arithmetic operations. In this chapter, we show how the natural error patterns in approximate computing can act as a built-in source of noise, cutting down the computational burden while still maintaining strong security protections. We discuss security definitions, possible threat models, and the kinds of error patterns most relevant to privacy-preserving machine learning, with a focus on balancing security and efficiency when combining approximate computing with existing privacy techniques. By exploring the connection between hardware-level design and cryptographic security, this chapter offers a pathway for making privacy-preserving machine learning more practical, scalable, and efficient in real-world use.