Training text embedding models under differential privacy constraints is challenging due to the high dimensionality of language data and the presence of rare, identifying linguistic features. We propose (Differentially Private Embedding Distillation), a framework that leverages teacher-student distillation with multi-layer noise injection to learn high-quality embeddings while providing differential privacy guarantees. DPED trains an ensemble of teacher models on disjoint subsets of sensitive text data, then transfers their knowledge to a student model through noisy aggregation at multiple layers. A rare-word-aware strategy adaptively handles infrequent words, improving privacy-utility trade-offs. Experiments on benchmark datasets demonstrate that DPED outperforms standard differentially private training methods, achieving substantially higher utility at the same privacy budget. Our approach protects individual word usage patterns in training documents, preventing models from memorizing unique linguistic fingerprints while maintaining practical utility for downstream NLP tasks. Source code is available at https://github.com/datasec-lab/DPED.