A Comprehensive Guide to Differential Privacy: From Theory to User Expectations
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917430681403392 |
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| author | Karmitsa, Napsu Airola, Antti Pahikkala, Tapio Pitkämäki, Tinja |
| author_facet | Karmitsa, Napsu Airola, Antti Pahikkala, Tapio Pitkämäki, Tinja |
| contents | The increasing availability of personal data has enabled significant advances in fields such as machine learning, healthcare, and cybersecurity. However, this data abundance also raises serious privacy concerns, especially in light of powerful re-identification attacks and growing legal and ethical demands for responsible data use. Differential privacy (DP) has emerged as a principled, mathematically grounded framework for mitigating these risks. This review provides a comprehensive survey of DP, covering its theoretical foundations, practical mechanisms, and real-world applications. It explores key algorithmic tools and domain-specific challenges - particularly in privacy-preserving machine learning and synthetic data generation. The report also highlights usability issues and the need for improved communication and transparency in DP systems. Overall, the goal is to support informed adoption of DP by researchers and practitioners navigating the evolving landscape of data privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03294 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Guide to Differential Privacy: From Theory to User Expectations Karmitsa, Napsu Airola, Antti Pahikkala, Tapio Pitkämäki, Tinja Cryptography and Security Artificial Intelligence Machine Learning 68P27, 68T09, 94A60 The increasing availability of personal data has enabled significant advances in fields such as machine learning, healthcare, and cybersecurity. However, this data abundance also raises serious privacy concerns, especially in light of powerful re-identification attacks and growing legal and ethical demands for responsible data use. Differential privacy (DP) has emerged as a principled, mathematically grounded framework for mitigating these risks. This review provides a comprehensive survey of DP, covering its theoretical foundations, practical mechanisms, and real-world applications. It explores key algorithmic tools and domain-specific challenges - particularly in privacy-preserving machine learning and synthetic data generation. The report also highlights usability issues and the need for improved communication and transparency in DP systems. Overall, the goal is to support informed adoption of DP by researchers and practitioners navigating the evolving landscape of data privacy. |
| title | A Comprehensive Guide to Differential Privacy: From Theory to User Expectations |
| topic | Cryptography and Security Artificial Intelligence Machine Learning 68P27, 68T09, 94A60 |
| url | https://arxiv.org/abs/2509.03294 |