Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model Performance
In the realm of artificial intelligence, the pursuit of enhanced model performance has often prioritized the exponential growth of training data, sometimes relegating concerns about data privacy. This approach has fostered a perception that data privacy and the achievement of high-performance AI mod...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11031447/ |
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| author | Alejandro de Leon Langure Mahdi Zareei |
| author_facet | Alejandro de Leon Langure Mahdi Zareei |
| author_sort | Alejandro de Leon Langure |
| collection | DOAJ |
| description | In the realm of artificial intelligence, the pursuit of enhanced model performance has often prioritized the exponential growth of training data, sometimes relegating concerns about data privacy. This approach has fostered a perception that data privacy and the achievement of high-performance AI models are inherently opposing goals, particularly as digital fingerprinting is increasingly presented as essential for personalized experiences. This study aims to challenge this notion by demonstrating that even straightforward anonymization preprocessing techniques do not substantially alter the performance of machine learning models, regardless of their initial capabilities, while simultaneously safeguarding user privacy. We trained four different machine learning models: linear regression, linear ridge regression, a neural network, and a BiLSTM network, and evaluated their performance in data sets with varying levels of k-anonymity, specifically comparing results from a K-index of 1 to 52. Our findings indicate that, while certain trade-offs may exist, they should not be considered significant enough to deter the integration of anonymization techniques in machine learning and AI research. This work advocates for the routine adoption of anonymization practices, supporting the premise that robust model performance and strong data privacy are not mutually exclusive objectives. |
| format | Article |
| id | doaj-art-cd6a5d11c2de40a392b2a0c37e0b6575 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-cd6a5d11c2de40a392b2a0c37e0b65752025-08-20T03:29:35ZengIEEEIEEE Access2169-35362025-01-011310590110591010.1109/ACCESS.2025.357895811031447Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model PerformanceAlejandro de Leon Langure0https://orcid.org/0000-0002-8362-2045Mahdi Zareei1https://orcid.org/0000-0001-6623-1758Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, MexicoTecnologico de Monterrey, School of Engineering and Sciences, Monterrey, MexicoIn the realm of artificial intelligence, the pursuit of enhanced model performance has often prioritized the exponential growth of training data, sometimes relegating concerns about data privacy. This approach has fostered a perception that data privacy and the achievement of high-performance AI models are inherently opposing goals, particularly as digital fingerprinting is increasingly presented as essential for personalized experiences. This study aims to challenge this notion by demonstrating that even straightforward anonymization preprocessing techniques do not substantially alter the performance of machine learning models, regardless of their initial capabilities, while simultaneously safeguarding user privacy. We trained four different machine learning models: linear regression, linear ridge regression, a neural network, and a BiLSTM network, and evaluated their performance in data sets with varying levels of k-anonymity, specifically comparing results from a K-index of 1 to 52. Our findings indicate that, while certain trade-offs may exist, they should not be considered significant enough to deter the integration of anonymization techniques in machine learning and AI research. This work advocates for the routine adoption of anonymization practices, supporting the premise that robust model performance and strong data privacy are not mutually exclusive objectives.https://ieeexplore.ieee.org/document/11031447/Data privacynatural language processingsentiment analysistext emotion detectionanonymization |
| spellingShingle | Alejandro de Leon Langure Mahdi Zareei Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model Performance IEEE Access Data privacy natural language processing sentiment analysis text emotion detection anonymization |
| title | Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model Performance |
| title_full | Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model Performance |
| title_fullStr | Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model Performance |
| title_full_unstemmed | Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model Performance |
| title_short | Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model Performance |
| title_sort | privacy preserving emotion detection evaluating the trade off between k anonymity and model performance |
| topic | Data privacy natural language processing sentiment analysis text emotion detection anonymization |
| url | https://ieeexplore.ieee.org/document/11031447/ |
| work_keys_str_mv | AT alejandrodeleonlangure privacypreservingemotiondetectionevaluatingthetradeoffbetweenkanonymityandmodelperformance AT mahdizareei privacypreservingemotiondetectionevaluatingthetradeoffbetweenkanonymityandmodelperformance |