Voice Fence Wall: User-optional voice privacy transmission
Sensors are widely applied in the collection of voice data. Since many attributes of voice data are sensitive such as user emotions, identity, raw voice collection may lead serious privacy threat. In the past, traditional feature extraction obtains and encrypts voice features that are then transmitt...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-03-01
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| Series: | Journal of Information and Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S294971592300080X |
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| _version_ | 1849412478900895744 |
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| author | Li Luo Yining Liu |
| author_facet | Li Luo Yining Liu |
| author_sort | Li Luo |
| collection | DOAJ |
| description | Sensors are widely applied in the collection of voice data. Since many attributes of voice data are sensitive such as user emotions, identity, raw voice collection may lead serious privacy threat. In the past, traditional feature extraction obtains and encrypts voice features that are then transmitted to upstream servers. In order to avoid sensitive attribute disclosure, it is necessary to separate the sensitive attributes from non-sensitive attributes of voice data. Motivated by this, user-optional privacy transmission framework for voice data (called: Voice Fence Wall) is proposed. Firstly, we provide user-optional, which means users can choose the attributes (sensitive attributes) they want to be protected. Secondly, Voice Fence Wall utilizes minimum mutual information (MI) to reduce the correlation between sensitive and non-sensitive attributes, thereby separating these attributes. Finally, only the separated non-sensitive attributes are transmitted to the upstream server, the quality of voice services is satisfied without leaking sensitive attributes. To verify the reliability and practicability, three voice datasets are used to evaluate the model, the experiments demonstrate that Voice Fence Wall not only effectively separates attributes to resist attribute inference attacks, but also outperforms related work in terms of classification performance. Specifically, our framework achieves 89.84 % accuracy in sentiment recognition and 6.01 % equal error rate in voice authentication. |
| format | Article |
| id | doaj-art-46e80237d26d496c9528ea685d465f86 |
| institution | Kabale University |
| issn | 2949-7159 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Information and Intelligence |
| spelling | doaj-art-46e80237d26d496c9528ea685d465f862025-08-20T03:34:26ZengKeAi Communications Co., Ltd.Journal of Information and Intelligence2949-71592024-03-012211612910.1016/j.jiixd.2023.12.002Voice Fence Wall: User-optional voice privacy transmissionLi Luo0Yining Liu1School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaCorresponding author.; School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaSensors are widely applied in the collection of voice data. Since many attributes of voice data are sensitive such as user emotions, identity, raw voice collection may lead serious privacy threat. In the past, traditional feature extraction obtains and encrypts voice features that are then transmitted to upstream servers. In order to avoid sensitive attribute disclosure, it is necessary to separate the sensitive attributes from non-sensitive attributes of voice data. Motivated by this, user-optional privacy transmission framework for voice data (called: Voice Fence Wall) is proposed. Firstly, we provide user-optional, which means users can choose the attributes (sensitive attributes) they want to be protected. Secondly, Voice Fence Wall utilizes minimum mutual information (MI) to reduce the correlation between sensitive and non-sensitive attributes, thereby separating these attributes. Finally, only the separated non-sensitive attributes are transmitted to the upstream server, the quality of voice services is satisfied without leaking sensitive attributes. To verify the reliability and practicability, three voice datasets are used to evaluate the model, the experiments demonstrate that Voice Fence Wall not only effectively separates attributes to resist attribute inference attacks, but also outperforms related work in terms of classification performance. Specifically, our framework achieves 89.84 % accuracy in sentiment recognition and 6.01 % equal error rate in voice authentication.http://www.sciencedirect.com/science/article/pii/S294971592300080XVoice collectionVoice Fence WallVoice privacyMutual information |
| spellingShingle | Li Luo Yining Liu Voice Fence Wall: User-optional voice privacy transmission Journal of Information and Intelligence Voice collection Voice Fence Wall Voice privacy Mutual information |
| title | Voice Fence Wall: User-optional voice privacy transmission |
| title_full | Voice Fence Wall: User-optional voice privacy transmission |
| title_fullStr | Voice Fence Wall: User-optional voice privacy transmission |
| title_full_unstemmed | Voice Fence Wall: User-optional voice privacy transmission |
| title_short | Voice Fence Wall: User-optional voice privacy transmission |
| title_sort | voice fence wall user optional voice privacy transmission |
| topic | Voice collection Voice Fence Wall Voice privacy Mutual information |
| url | http://www.sciencedirect.com/science/article/pii/S294971592300080X |
| work_keys_str_mv | AT liluo voicefencewalluseroptionalvoiceprivacytransmission AT yiningliu voicefencewalluseroptionalvoiceprivacytransmission |