To address the dual challenges of data privacy and project heterogeneity in cross-project software defect prediction, this paper proposes an innovative framework named PRIDE-SDP. The core contribution of this framework lies in the deep integration of three key technologies: adopting a personalized federated learning paradigm to customize dedicated prediction models for each heterogeneous project, integrating (ε,δ)-differential privacy mechanisms that provide rigorous mathematical guarantees to protect data locally, and designing a dedicated Temporal-Contextual Fusion Network (TCFN) to efficiently capture software metric features. Experiments on six dataset groups covering 27 open-source projects and 3 enterprise projects validate the effectiveness of this framework: compared with state-of-the-art cross-project defect prediction baselines, PRIDE-SDP achieves an average AUC improvement of 10.7% and F1-score improvement of 7.3%; performance on enterprise datasets is even more outstanding, with an average MCC improvement of 45.2%, Effort@20% improvement of 29.5%, and F1-Score improvement of 35.4% compared to advanced baseline methods. Meanwhile, when providing strong privacy guarantees, the framework's average performance retention rate can still reach over 98% of the optimal performance, and in membership inference attack experiments, it can reduce the attacker's attack accuracy by an average of more than 36%. Experimental results demonstrate that PRIDE-SDP effectively balances privacy protection and personalized adaptation capabilities while maintaining high performance.