# Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design

arXiv: https://arxiv.org/abs/2210.07817 · HTML: https://mlsanity.com/paper/2210.07817/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Influence Function based Data Poisoning Attacks to Top-N Recommender Systems](https://mlsanity.com/paper/2002.08025/) (2020)
- [Adversarial Personalized Ranking for Recommendation](https://mlsanity.com/paper/1808.03908/) (2018)
- [Adversarial Training Towards Robust Multimedia Recommender System](https://mlsanity.com/paper/1809.07062/) (2018)
- [Privacy in Social Media: Identification, Mitigation and Applications](https://mlsanity.com/paper/1808.02191/) (2018)
- [Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start](https://mlsanity.com/paper/2006.01888/) (2020)
- [Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack](https://mlsanity.com/paper/2107.10457/) (2021)

## Abstract (arXiv)

Information has exploded on the Internet and mobile with the advent of the big data era. In particular, recommendation systems are widely used to help consumers who struggle to select the best products among such a large amount of information. However, recommendation systems are vulnerable to malicious user biases, such as fake reviews to promote or demote specific products, as well as attacks that steal personal information. Such biases and attacks compromise the fairness of the recommendation model and infringe the privacy of users and systems by distorting data.Recently, deep-learning collaborative filtering recommendation systems have shown to be more vulnerable to this bias. In this position paper, we examine the effects of bias that cause various ethical and social issues, and discuss the need for designing the robust recommendation system for fairness and stability.