Privacy-Aware Speech Recognition Using Secure AI Models

Abstract

This project focuses on developing a privacy-aware speech recognition system using secure AI models to protect users\' speech data. The system ensures data confidentiality by employing Federated Learning (FL), Homomorphic Encryption (HE), and Differential Privacy (DP). These techniques allow the model to processandtrainonencrypteddatawithout compromising privacy. The system also minimizes data transmission to cloud servers, reducing the risk of data breaches. The results demonstrate high speech recognition accuracy while maintaining strong data privacy, making it ideal for secure voice-enabled applications.

Introduction

1. Overview

Speech recognition technology is widely used in virtual assistants, smart devices, and customer service, but it raises serious privacy concerns due to the risk of exposing sensitive user data. This project aims to develop a Privacy-Aware Speech Recognition System that integrates advanced security techniques:

• Homomorphic Encryption (HE) – processes encrypted data without decryption.

• Federated Learning (FL) – trains models locally on user devices, not centralized servers.

• Differential Privacy (DP) – adds noise to anonymize speech data.

2. Literature Review Insights

Multiple studies have shown:

• HE enables secure inference with 92–94% accuracy.

• FL allows private training with 93% accuracy and no raw data sharing.

• DP ensures anonymity and strong privacy (up to 95% accuracy).

• Combining HE, FL, and DP enhances security without sacrificing model performance.

• Edge AI (on-device processing) significantly reduces cloud dependency and breach risk.

3. Problem Statement

Most current speech recognition systems transmit raw audio to centralized servers, increasing the risk of data breaches, unauthorized access, and user mistrust. They often lack built-in privacy mechanisms like HE, FL, or DP. The core problem is the lack of strong privacy safeguards during both training and inference.

4. Project Objectives

• Develop a secure, decentralized, and accurate speech recognition system.

• Ensure:

  • Encrypted inference with HE.

  • Local training using FL.

  • Data anonymization through DP.

• Target ≥90% recognition accuracy.

• Minimize cloud usage via Edge AI.

• Provide a scalable and deployable solution for devices like mobile phones and smart assistants.

5. Methodology

• Use TensorFlow or PyTorch for model development.

• Employ HE for encrypted inference, FL for local training, and DP for privacy protection.

• Run speech processing on devices (Edge AI) to reduce data exposure.

• Implement secure APIs with AES and TLS for protected communication.

• Evaluate performance with datasets like Google Speech Commands and Mozilla Common Voice.

• Conduct privacy risk assessments (e.g., STRIDE, MITRE ATT&CK).

• Compare with traditional models based on:

  • Accuracy

  • Encryption overhead

  • Latency

  • Privacy strength

Conclusion

This project, titled \"Privacy-Aware Speech Recognition Using Secure AI Models,\" addresses growing privacy concerns in voice-enabled technologies by developing a semcure speech-to-text system using Homomorphic Encryption (HE), Federated Learning (FL), Differential Privacy (DP), and Edge AI. By enabling encrypted inference through HE, local training via FL, and anonymization through DP, the system ensuresthatrawuseraudioisneverexposedduring training or inference. Edge AI reducesrelianceoncloudservers,enhancing privacy and speed, while secure APIs with AES encryption protect data during transmission.Evaluationsusingdatasetslike Google Speech Commands and Mozilla Common Voice showed the system maintained over 90% accuracy, proving that strongprivacymeasures do notsignificantly compromise performance. Compared to traditional models, this privacy-aware system reduced risks of data breaches, identity leakage, and unauthorized access, while remaining resilient to cyber threats as verified through threat modeling and risk assessments. The system is scalable and suitable for applications in smart devices, healthcare, and customer service, setting a foundation for future AI systems that prioritize data confidentiality without sacrificing efficiency or accuracy.

References

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Copyright

Copyright © 2025 Shreshth Sharma, Pankaj , Priyansh , Raj Aggarwal , Ashmit Chauhan, Dr. Vinod Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.