Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication
Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, w...
Ausführliche Beschreibung
Gespeichert in:
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Guo, Yunxi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of hardware and systems security - [Cham] : Springer International Publishing, 2017, 1(2017), 1 vom: März, Seite 18-37 |
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| Übergeordnetes Werk: |
volume:1 ; year:2017 ; number:1 ; month:03 ; pages:18-37 |
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| DOI / URN: |
10.1007/s41635-017-0003-4 |
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| Katalog-ID: |
SPR038255774 |
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| 520 | |a Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. | ||
| 650 | 4 | |a Physical unclonable functions |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mobile authentication |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Speech processing |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tyagi, Akhilesh |4 aut | |
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10.1007/s41635-017-0003-4 doi (DE-627)SPR038255774 (SPR)s41635-017-0003-4-e DE-627 ger DE-627 rakwb eng Guo, Yunxi verfasserin aut Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer 2017 Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. Physical unclonable functions (dpeaa)DE-He213 Mobile authentication (dpeaa)DE-He213 Speech processing (dpeaa)DE-He213 Tyagi, Akhilesh aut Enthalten in Journal of hardware and systems security [Cham] : Springer International Publishing, 2017 1(2017), 1 vom: März, Seite 18-37 (DE-627)885200780 (DE-600)2892763-1 2509-3436 nnns volume:1 year:2017 number:1 month:03 pages:18-37 https://dx.doi.org/10.1007/s41635-017-0003-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2017 1 03 18-37 |
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10.1007/s41635-017-0003-4 doi (DE-627)SPR038255774 (SPR)s41635-017-0003-4-e DE-627 ger DE-627 rakwb eng Guo, Yunxi verfasserin aut Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer 2017 Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. Physical unclonable functions (dpeaa)DE-He213 Mobile authentication (dpeaa)DE-He213 Speech processing (dpeaa)DE-He213 Tyagi, Akhilesh aut Enthalten in Journal of hardware and systems security [Cham] : Springer International Publishing, 2017 1(2017), 1 vom: März, Seite 18-37 (DE-627)885200780 (DE-600)2892763-1 2509-3436 nnns volume:1 year:2017 number:1 month:03 pages:18-37 https://dx.doi.org/10.1007/s41635-017-0003-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2017 1 03 18-37 |
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10.1007/s41635-017-0003-4 doi (DE-627)SPR038255774 (SPR)s41635-017-0003-4-e DE-627 ger DE-627 rakwb eng Guo, Yunxi verfasserin aut Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer 2017 Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. Physical unclonable functions (dpeaa)DE-He213 Mobile authentication (dpeaa)DE-He213 Speech processing (dpeaa)DE-He213 Tyagi, Akhilesh aut Enthalten in Journal of hardware and systems security [Cham] : Springer International Publishing, 2017 1(2017), 1 vom: März, Seite 18-37 (DE-627)885200780 (DE-600)2892763-1 2509-3436 nnns volume:1 year:2017 number:1 month:03 pages:18-37 https://dx.doi.org/10.1007/s41635-017-0003-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2017 1 03 18-37 |
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10.1007/s41635-017-0003-4 doi (DE-627)SPR038255774 (SPR)s41635-017-0003-4-e DE-627 ger DE-627 rakwb eng Guo, Yunxi verfasserin aut Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer 2017 Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. Physical unclonable functions (dpeaa)DE-He213 Mobile authentication (dpeaa)DE-He213 Speech processing (dpeaa)DE-He213 Tyagi, Akhilesh aut Enthalten in Journal of hardware and systems security [Cham] : Springer International Publishing, 2017 1(2017), 1 vom: März, Seite 18-37 (DE-627)885200780 (DE-600)2892763-1 2509-3436 nnns volume:1 year:2017 number:1 month:03 pages:18-37 https://dx.doi.org/10.1007/s41635-017-0003-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2017 1 03 18-37 |
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10.1007/s41635-017-0003-4 doi (DE-627)SPR038255774 (SPR)s41635-017-0003-4-e DE-627 ger DE-627 rakwb eng Guo, Yunxi verfasserin aut Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer 2017 Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. Physical unclonable functions (dpeaa)DE-He213 Mobile authentication (dpeaa)DE-He213 Speech processing (dpeaa)DE-He213 Tyagi, Akhilesh aut Enthalten in Journal of hardware and systems security [Cham] : Springer International Publishing, 2017 1(2017), 1 vom: März, Seite 18-37 (DE-627)885200780 (DE-600)2892763-1 2509-3436 nnns volume:1 year:2017 number:1 month:03 pages:18-37 https://dx.doi.org/10.1007/s41635-017-0003-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2017 1 03 18-37 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR038255774</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230328195935.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s41635-017-0003-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR038255774</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s41635-017-0003-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Guo, Yunxi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. 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Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication Physical unclonable functions (dpeaa)DE-He213 Mobile authentication (dpeaa)DE-He213 Speech processing (dpeaa)DE-He213 |
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voice-based user-device physical unclonable functions for mobile device authentication |
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Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication |
| abstract |
Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. © Springer 2017 |
| abstractGer |
Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. © Springer 2017 |
| abstract_unstemmed |
Abstract In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests. © Springer 2017 |
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1 |
| title_short |
Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication |
| url |
https://dx.doi.org/10.1007/s41635-017-0003-4 |
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true |
| author2 |
Tyagi, Akhilesh |
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Tyagi, Akhilesh |
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| doi_str |
10.1007/s41635-017-0003-4 |
| up_date |
2024-07-03T17:01:59.135Z |
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|
| score |
7.400094 |