# When Machine Learning Meets Privacy: A Survey and Outlook

arXiv: https://arxiv.org/abs/2011.11819 · HTML: https://mlsanity.com/paper/2011.11819/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 30 of 68)

- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning](https://mlsanity.com/paper/2212.10986/) (2022)
- [Survey: Leakage and Privacy at Inference Time](https://mlsanity.com/paper/2107.01614/) (2021)
- [SoK: Privacy-Preserving Data Synthesis](https://mlsanity.com/paper/2307.02106/) (2023)
- [Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI](https://mlsanity.com/paper/2105.12497/) (2021)
- [DP-Image: Differential Privacy for Image Data in Feature Space](https://mlsanity.com/paper/2103.07073/) (2021)
- [OPOM: Customized Invisible Cloak towards Face Privacy Protection](https://mlsanity.com/paper/2205.11981/) (2022)
- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [The privacy issue of counterfactual explanations: explanation linkage attacks](https://mlsanity.com/paper/2210.12051/) (2022)
- [Differential Privacy has Bounded Impact on Fairness in Classification](https://mlsanity.com/paper/2210.16242/) (2022)
- [Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations](https://mlsanity.com/paper/2202.13295/) (2022)
- [FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning](https://mlsanity.com/paper/2307.13214/) (2023)
- [GELU Activation Function in Deep Learning: A Comprehensive Mathematical Analysis and Performance](https://mlsanity.com/paper/2305.12073/) (2023)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Machine Learning with Confidential Computing: A Systematization of Knowledge](https://mlsanity.com/paper/2208.10134/) (2022)
- [A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks](https://mlsanity.com/paper/2406.06186/) (2024)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Differential Privacy Mechanisms in Neural Tangent Kernel Regression](https://mlsanity.com/paper/2407.13621/) (2024)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey](https://mlsanity.com/paper/2309.16398/) (2023)
- [Episodic memory in AI agents poses risks that should be studied and mitigated](https://mlsanity.com/paper/2501.11739/) (2025)
- [Toward a Taxonomy of Trust for Probabilistic Machine Learning](https://mlsanity.com/paper/2112.03270/) (2021)
- [Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity](https://mlsanity.com/paper/2211.02139/) (2022)
- [Byzantine-Robust Aggregation for Securing Decentralized Federated Learning](https://mlsanity.com/paper/2409.17754/) (2024)
- [A Principles-based Ethics Assurance Argument Pattern for AI and Autonomous Systems](https://mlsanity.com/paper/2203.15370/) (2022)
- [When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes](https://mlsanity.com/paper/2207.08336/) (2022)
- [MobileTL: On-device Transfer Learning with Inverted Residual Blocks](https://mlsanity.com/paper/2212.03246/) (2022)

## Abstract (arXiv)

The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning (ML) is still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This paper surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.