Efficient and Privacy-preserving Voice-based Search over mHealth Data

TL;DR

This paper introduces a privacy-preserving voice-based search system for mHealth data that maintains voice data privacy using homomorphic encryption while enabling accurate search based on voice features.

Contribution

It presents a novel scheme combining homomorphic encryption with voice feature similarity for efficient, privacy-preserving voice search in healthcare applications.

Findings

Achieves 80.8% accuracy in voice matching

Preserves voice data privacy during search

Enables efficient voice-based queries in healthcare

Abstract

In-home IoT devices play a major role in healthcare systems as smart personal assistants. They usually come with a voice-enabled feature to add an extra level of usability and convenience to elderly, disabled people, and patients. In this paper, we propose an efficient and privacy-preserving voice-based search scheme to enhance the efficiency and the privacy of in-home healthcare applications. We consider an application scenario where patients use the devices to record and upload their voice to servers and the caregivers search the interested voices of their patient's based on the voice content, mood, tone and background sound. Our scheme preserves the richness and privacy of voice data and enables accurate and efficient voice-based search, while in current systems that use speech recognition the richness and privacy of voice data are compromised. Specifically, our scheme achieves the privacy by employing a homomorphic encryption; only encrypted voice data is uploaded to the server who is unable to access the original voice data. In addition, our scheme enables the server to selectively and accurately respond to caregiver's queries on the voice data based on voice's feature similarity. We evaluate our scheme through real experiments and show that our scheme even with privacy preservation can successfully match similar voice data at an average accuracy of 80.8%.