Privacy-Preserving Analytics in HR Tech- Federated Learning and Differential Privacy Techniques for Sensitive Data

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This paper explores the application of privacy-preserving analytics in human resources (HR), focusing on the synergistic use of federated learning and differential privacy. As HR departments increasingly leverage data-driven insights, the protection of sensitive employee information becomes paramount. Federated learning enables collaborative model training without centralizing raw data, while differential privacy adds calibrated noise to ensure individual data remains indiscernible. Together, these techniques form a robust framework for safeguarding HR data while enabling advanced analytics. The paper discusses the challenges of handling sensitive HR information, examines the implementation of federated learning and differential privacy, and demonstrates their combined effectiveness in maintaining data utility while ensuring privacy. By adopting these approaches, organizations can derive valuable workforce insights, comply with data protection regulations, and foster employee trust. This research contributes to the growing field of ethical data use in HR, offering a blueprint for balancing analytical capabilities with privacy imperatives in the modern workplace. Keywords—Privacy-preserving analytics, Federated learning, Differential privacy, HR analytics, Data protection, Employee privacy, Decentralized learning, GDPR compliance, CCPA compliance, Sensitive data handling, Data-driven HR, Privacy-utility trade-off.