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Enhancing Privacy in Voice Assistants: A Risk-Based Approach to Mitigation Strategies

Research Article

Authors: Radwan Eskhita; Andren Heeb; Elias Gmuender; Vijaya Kittu Manda

DOI: https://doi.org/10.7759/s44389-025-00074-8

Journal Name: Cureus Journal of Computer Science; ISSN: EISSN: 3005-1487

Publisher: SpringerNature

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Publication Date: May 29, 2026

Pages: 23 pages

Abstract: The growing use of voice-activated personal assistants (VAPAs) has introduced significant user privacy and data security challenges. This study investigates privacy vulnerabilities in VAPAs using a mixed-methods approach that combines simulated experiments with structured risk assessment. Through controlled simulations, we evaluate vulnerabilities of VAPAs to various attack vectors, including adversarial attacks on automatic speech recognition (ASR), voice cloning, and side-channel attacks. A risk assessment framework using DREAD (Damage, Reproducibility, Exploitability, Affected Users, Discoverability) and STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) models quantifies the identified vulnerabilities. Results reveal that VAPAs are highly vulnerable to adversarial attacks (85-90% success rate), and voice cloning can effectively circumvent authentication measures (up to 92% authentication bypass rate). The discussion analyzes these findings in the context of existing literature, highlighting the limitations of current security measures. The paper concludes with mitigation strategies to enhance user privacy and data security in VAPAs. These include enhanced authentication methods, improved ASR robustness, data minimization techniques, robust security protocols, and proactive threat detection systems. Addressing privacy risks in VAPAs requires a multi-faceted approach that integrates technological innovations, sound regulatory policies, and comprehensive user education initiatives. The study has practical applications for technology developers and VAPA providers.

Keywords: automatic speech recognition, voice cloning, voice-activated personal assistants, adversarial attacks, privacy vulnerability

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