{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T07:46:20Z","timestamp":1763451980101,"version":"3.45.0"},"reference-count":78,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Security and Privacy"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The intersection of quantum computing and voice technology offers unparalleled security challenges and opportunities. In this paper, we propose\n                    <jats:styled-content>quantum voice security<\/jats:styled-content>\n                    as an interdisciplinary research field, bridging vulnerabilities where quantum algorithms breach classical voice protection mechanisms while enabling new defense mechanisms. We define a mathematical model for quantum based threats: (1) As\n                    <jats:styled-content>cryptographic collapse<\/jats:styled-content>\n                    by Shor's algorithm, breaching RSA\/ECC (elliptic\u2010curve cryptography) protected voice biometrics and communications in polynomial time, enabling store now, decrypt later (SNDL) attacks; (2) As in\n                    <jats:styled-content>adversarial amplification<\/jats:styled-content>\n                    by Grover optimized perturbations in Fock space using entanglement monogamy for cross\u2010model transferability; (3) In\n                    <jats:styled-content>biometric spoofing<\/jats:styled-content>\n                    , by relativistic Quantum Generative Adversarial Networks (RQGANs), producing voiceprints indistinguishable from validated data under trace distance (TD) metrics. Concurrently, we define post\u2010quantum mitigation solutions: (i) In\n                    <jats:styled-content>lattice based cryptosystems<\/jats:styled-content>\n                    , (RLWE (Ring Learning With Errors) with \u2010bit strength) safeguarding voiceprint encryption against quantum decoherence; (ii) in\n                    <jats:styled-content>quantum based detection<\/jats:styled-content>\n                    , by Wigner function negativity () and holographic entropy bound violation () to identify deepfakes; (iii) In\n                    <jats:styled-content>hybrid authentication protocols<\/jats:styled-content>\n                    (e.g., entanglement\u2010swapped TLS\u2010QUIC (Transport Layer Security over QUIC)) with composable security . Experimental results on multiple audio datasets (\n                    <jats:styled-content>LibriSpeech<\/jats:styled-content>\n                    ,\n                    <jats:styled-content>Brahms<\/jats:styled-content>\n                    ) validate quantum vulnerabilities\u2014achieving 99.4% spoof detection accuracy and RLWE fault tolerance against \u2010stable noise. Our work integrates lattice cryptography, quantum machine learning (QML), and information theory to reshape voice security models for the quantum age. The proposed RLWE based cryptosystem obtained 98.6% encryption fidelity and 95.2% spoof detection on real\u2010world datasets, which is 6%\u20137% more accurate and 10%\u201312% more reliable against quantum noise compared to classical baselines.\n                  <\/jats:p>","DOI":"10.1002\/spy2.70106","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T09:34:15Z","timestamp":1758792855000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum Voice\u2010Based Cyber\u2010Attacks: Threats, Vulnerabilities, and Post\u2010Quantum Mitigation Strategies"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2096-8273","authenticated-orcid":false,"given":"Shyam R.","family":"Sihare","sequence":"first","affiliation":[{"name":"Department of Computer Science and Application Dr. APJ Abdul Kalam Goverment College  Silvassa India"}]}],"member":"311","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"volume-title":"Designing Voice User Interfaces: Principles of Conversational Experiences","year":"2016","author":"Pearl C.","key":"e_1_2_15_2_1"},{"key":"e_1_2_15_3_1","first-page":"1","article-title":"A Comprehensive Review of Privacy Risks in Voice\u2010Activated Virtual Assistants","author":"Ayisha T.","year":"2024","journal-title":"International Journal of Global Innovations and Solutions (IJGIS)"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21072312"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3701985"},{"key":"e_1_2_15_6_1","doi-asserted-by":"publisher","DOI":"10.22331\/q-2018-08-06-79"},{"key":"e_1_2_15_7_1","doi-asserted-by":"publisher","DOI":"10.47974\/JDMSC-2078"},{"volume-title":"Quantum Machine Learning: What Quantum Computing Means to Data Mining","year":"2014","author":"Wittek P.","key":"e_1_2_15_8_1"},{"key":"e_1_2_15_9_1","doi-asserted-by":"crossref","unstructured":"M.NorvalandZ.Wang \u201cQuantum ai in Speech Emotion Recognition \u201d(2024).","DOI":"10.21203\/rs.3.rs-4894795\/v1"},{"key":"e_1_2_15_10_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0036144598347011"},{"key":"e_1_2_15_11_1","doi-asserted-by":"publisher","DOI":"10.34306\/itsdi.v5i2.662"},{"key":"e_1_2_15_12_1","doi-asserted-by":"crossref","unstructured":"L. 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