# Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning Against Attribute Inference Attacks

arXiv: https://arxiv.org/abs/2312.06989 · HTML: https://mlsanity.com/paper/2312.06989/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2023.

## Built on by (most cited first, 13 of 13)

- [Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks](https://mlsanity.com/paper/2403.02116/) (2024)
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- [Measure-Theoretic Anti-Causal Representation Learning](https://mlsanity.com/paper/2510.18052/) (2025)
- [Learning Robust and Privacy-Preserving Representations via Information Theory](https://mlsanity.com/paper/2412.11066/) (2024)
- [HearSay Benchmark: Do Audio LLMs Leak What They Hear?](https://mlsanity.com/paper/2601.03783/) (2026)
- [Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence](https://mlsanity.com/paper/2407.14710/) (2024)
- [BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation](https://mlsanity.com/paper/2412.02934/) (2024)
- [ERIS: Enhancing the Privacy-Utility-Scalability Trade-Off in Federated Learning via Federated Shard Aggregation](https://mlsanity.com/paper/2602.08617/) (2026)
- [Convergent Differential Privacy Analysis for General Federated Learning](https://mlsanity.com/paper/2408.15621/) (2024)
- [Multi-Hop Privacy Propagation for Differentially Private Federated Learning in Social Networks](https://mlsanity.com/paper/2508.07676/) (2025)
- [InfoDecom: Decomposing Information for Defending Against Privacy Leakage in Split Inference](https://mlsanity.com/paper/2511.13365/) (2025)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)

## Abstract (arXiv)

Federated learning (FL) has been widely studied recently due to its property to collaboratively train data from different devices without sharing the raw data. Nevertheless, recent studies show that an adversary can still be possible to infer private information about devices' data, e.g., sensitive attributes such as income, race, and sexual orientation. To mitigate the attribute inference attacks, various existing privacy-preserving FL methods can be adopted/adapted. However, all these existing methods have key limitations: they need to know the FL task in advance, or have intolerable computational overheads or utility losses, or do not have provable privacy guarantees. We address these issues and design a task-agnostic privacy-preserving presentation learning method for FL (bf TAPPFL) against attribute inference attacks. TAPPFL is formulated via information theory. Specifically, TAPPFL has two mutual information goals, where one goal learns task-agnostic data representations that contain the least information about the private attribute in each device's data, and the other goal ensures the learnt data representations include as much information as possible about the device data to maintain FL utility. We also derive privacy guarantees of TAPPFL against worst-case attribute inference attacks, as well as the inherent tradeoff between utility preservation and privacy protection. Extensive results on multiple datasets and applications validate the effectiveness of TAPPFL to protect data privacy, maintain the FL utility, and be efficient as well. Experimental results also show that TAPPFL outperforms the existing defensesfootnoteSource code and full version: urlhttps://github.com/TAPPFL.