A Comprehensive Guide to Differential Privacy: From Theory to User Expectations

2025-09-03 · Napsu Karmitsa, Antti Airola, Tapio Pahikkala, Tinja Pitkämäki arxiv

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.

📄 PDF Abstract BibTeX arXiv:2509.03294

Code (0)

등록된 구현이 없습니다.

Tasks

Synthetic Data Generation

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

2022-09-04 · Jiaqian Ren, Lei Jiang, Hao Peng, Lingjuan Lyu 외

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately …

Attribute Link Prediction Network Embedding Privacy Preserving

Fairness-aware Differentially Private Collaborative Filtering

2023-03-16 · Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang 외

Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic f…

Collaborative Filtering Fairness Privacy Preserving

Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via $f$-Differential Privacy

2025-10-22 · Xiang Li, Buxin Su, Chendi Wang, Qi Long 외 arxiv

Differentially private (DP) decentralized Federated Learning (FL) allows local users to collaborate without sharing their data with a central server. However, accurately quantifying the privacy budget of private FL algor…

Federated Learning

Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems

2025-11-27 · Shiva Parsarad, Isabel Wagner arxiv

Recommender systems (RSs) output ranked lists of items, such as movies or restaurants, that users may find interesting, based on the user's past ratings and ratings from other users. RSs increasingly incorporate differen…

Collaborative Filtering

Graph Structure Learning with Privacy Guarantees for Open Graph Data

2025-07-25 · Muhao Guo, Jiaqi Wu, Yizheng Liao, Wenke Lee 외 arxiv

Publishing open graph data while preserving individual privacy remains challenging when data publishers and data users are distinct entities. Although differential privacy (DP) provides rigorous guarantees, most existing…

Graph structure learning