# AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning

arXiv: https://arxiv.org/abs/1805.04810 · HTML: https://mlsanity.com/paper/1805.04810/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2018.

## Built on by (most cited first, 30 of 60)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning](https://mlsanity.com/paper/2108.11023/) (2021)
- [Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems](https://mlsanity.com/paper/2401.05778/) (2024)
- [Survey: Leakage and Privacy at Inference Time](https://mlsanity.com/paper/2107.01614/) (2021)
- [Quantifying and Mitigating Privacy Risks of Contrastive Learning](https://mlsanity.com/paper/2102.04140/) (2021)
- [Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis](https://mlsanity.com/paper/2306.07754/) (2023)
- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective](https://mlsanity.com/paper/2311.15792/) (2023)
- [Inferring Sensitive Attributes from Model Explanations](https://mlsanity.com/paper/2208.09967/) (2022)
- [Machine Learning with Confidential Computing: A Systematization of Knowledge](https://mlsanity.com/paper/2208.10134/) (2022)
- [Adversarial Robustness for Tabular Data through Cost and Utility Awareness](https://mlsanity.com/paper/2208.13058/) (2022)
- [Purifier: Defending Data Inference Attacks via Transforming Confidence Scores](https://mlsanity.com/paper/2212.00612/) (2022)
- [Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems](https://mlsanity.com/paper/2310.05847/) (2023)
- [Get a Model! Model Hijacking Attack Against Machine Learning Models](https://mlsanity.com/paper/2111.04394/) (2021)
- [Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance](https://mlsanity.com/paper/2402.01096/) (2024)
- [Evaluating LLM-based Personal Information Extraction and Countermeasures](https://mlsanity.com/paper/2408.07291/) (2024)
- [Input-Specific Robustness Certification for Randomized Smoothing](https://mlsanity.com/paper/2112.12084/) (2021)
- [SoK: Machine Learning Governance](https://mlsanity.com/paper/2109.10870/) (2021)
- [A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection](https://mlsanity.com/paper/2302.03654/) (2023)
- [Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective](https://mlsanity.com/paper/2101.03042/) (2021)
- [On the Alignment of Group Fairness with Attribute Privacy](https://mlsanity.com/paper/2211.10209/) (2022)
- [User Consented Federated Recommender System Against Personalized Attribute Inference Attack](https://mlsanity.com/paper/2312.16203/) (2023)
- [Synthetic Data: Revisiting the Privacy-Utility Trade-off](https://mlsanity.com/paper/2407.07926/) (2024)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [10 Security and Privacy Problems in Large Foundation Models](https://mlsanity.com/paper/2110.15444/) (2021)
- [When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges](https://mlsanity.com/paper/2201.04677/) (2022)
- [Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks](https://mlsanity.com/paper/2107.10302/) (2021)
- [Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks](https://mlsanity.com/paper/2202.02242/) (2022)
- [State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey](https://mlsanity.com/paper/2404.16847/) (2024)
- [Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey](https://mlsanity.com/paper/2105.06300/) (2021)

## Abstract (arXiv)

Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defenses leverage game theory or heuristics based on correlations between the public data and attributes. These defenses are not practical. Specifically, game-theoretic defenses require solving intractable optimization problems, while correlation-based defenses incur large utility loss of users' public data. In this paper, we present AttriGuard, a practical defense against attribute inference attacks. AttriGuard is computationally tractable and has small utility loss. Our AttriGuard works in two phases. Suppose we aim to protect a user's private attribute. In Phase I, for each value of the attribute, we find a minimum noise such that if we add the noise to the user's public data, then the attacker's classifier is very likely to infer the attribute value for the user. We find the minimum noise via adapting existing evasion attacks in adversarial machine learning. In Phase II, we sample one attribute value according to a certain probability distribution and add the corresponding noise found in Phase I to the user's public data. We formulate finding the probability distribution as solving a constrained convex optimization problem. We extensively evaluate AttriGuard and compare it with existing methods using a real-world dataset. Our results show that AttriGuard substantially outperforms existing methods. Our work is the first one that shows evasion attacks can be used as defensive techniques for privacy protection.