Manipulating Recommender Systems: A Comprehensive Survey of Poisoning Attacks and Countermeasures

Kernekoncepter

Poisoning attacks on recommender systems pose a serious threat by manipulating the training data to corrupt the integrity of the underlying models, leading to biased recommendations that benefit the attacker's goals.

Resumé

This survey provides a comprehensive overview of the state-of-the-art in poisoning attacks on recommender systems and the countermeasures to detect and prevent them.
The key highlights are:

A novel taxonomy of poisoning attacks is presented, which formally defines five dimensions: the adversary's goal, knowledge, capabilities, impact, and approach. This taxonomy helps to organize the 30+ attacks described in the literature.

Model-agnostic poisoning attacks are reviewed, which can be executed against any recommender system regardless of the underlying algorithm. These attacks involve injecting manipulated data into the training set to bias the model's recommendations.

Model-intrinsic poisoning attacks are examined, which target specific types of recommender systems by exploiting vulnerabilities in their training processes. These attacks can cause substantial damage to the underlying models.

Over 40 countermeasures to detect and prevent poisoning attacks are analyzed, and their effectiveness against specific types of attacks is evaluated. This provides insights into the strengths and weaknesses of different mitigation strategies.

Open research challenges and promising future directions are discussed, such as addressing concept drift, handling imbalanced data, and securing recommender systems across diverse application domains like e-commerce, social media, and news recommendations.

Statistik

"Recommender system market to increase from US$1.14 billion to US$12.03 billion by 2025."
"Fake reviews are a well-documented example of poisoning attacks to increase product recommendations."

Citater

"Poisoning attacks can seriously undermine the commercial success of any company falling victim to such an attack."
"Poisoning attacks pose a more severe threat to economies and society compared to profile pollution attacks."
"Recommender systems are typically public and accessible to large numbers of users, making them very vulnerable to poisoning attacks."

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Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures

by Thanh Toan N... kl. arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14942.pdf

Dybere Forespørgsler

How can the trade-off between the attacker's capability and the system's robustness be better understood and quantified?

In order to better understand and quantify the trade-off between the attacker's capability and the system's robustness in the context of poisoning attacks on recommender systems, several key factors need to be considered:

Attack Surface Analysis: Conducting a thorough analysis of the potential vulnerabilities in the recommender system that could be exploited by an attacker. This includes understanding the system's architecture, data sources, and algorithms used.

Adversary Modeling: Developing detailed models of potential adversaries, including their knowledge, capabilities, and objectives. This can help in assessing the likelihood and impact of different types of attacks.

Risk Assessment: Evaluating the potential risks associated with different types of poisoning attacks, considering the attacker's capabilities and the system's vulnerabilities. This involves quantifying the potential impact of successful attacks on the system.

Simulation and Testing: Conducting simulations and testing to assess the system's resilience against different types of attacks. This can help in understanding how the system behaves under different attack scenarios and the trade-offs involved.

Metrics and Evaluation: Defining metrics to measure the system's robustness and the effectiveness of countermeasures against poisoning attacks. This can include metrics related to accuracy, fairness, and security of the recommender system.

By taking a comprehensive approach that considers these factors, the trade-off between the attacker's capability and the system's robustness can be better understood and quantified. This can help in developing effective strategies to mitigate the risks posed by poisoning attacks on recommender systems.

What are the potential societal implications of successful poisoning attacks on recommender systems used in domains like news recommendations and social media?

Successful poisoning attacks on recommender systems in domains like news recommendations and social media can have significant societal implications:

Spread of Misinformation: Poisoning attacks can lead to the spread of misinformation and fake news, influencing public opinion and potentially causing social unrest.

Manipulation of Public Discourse: By promoting certain content and demoting others, attackers can manipulate public discourse and shape the narrative on important issues.

Erosion of Trust: If users become aware of manipulation in the recommendations they receive, it can erode trust in the platform and the information presented, leading to a loss of credibility.

Polarization: Poisoning attacks can exacerbate existing social divisions by reinforcing echo chambers and filter bubbles, limiting exposure to diverse viewpoints.

Impact on Democracy: In the context of news recommendations, poisoning attacks can impact democratic processes by influencing public opinion, voter behavior, and political outcomes.

Financial Consequences: In e-commerce platforms, poisoning attacks can lead to financial losses for businesses by promoting or demoting products based on malicious intent rather than genuine user preferences.

Psychological Effects: Manipulated recommendations can also have psychological effects on users, influencing their perceptions, beliefs, and behaviors.

Overall, successful poisoning attacks on recommender systems in these domains can have far-reaching consequences on society, affecting information integrity, public discourse, trust in online platforms, and even democratic processes.

Can techniques from other fields like federated learning or differential privacy be adapted to build more resilient and trustworthy recommender systems?

Techniques from fields like federated learning and differential privacy can indeed be adapted to build more resilient and trustworthy recommender systems:

Federated Learning: By leveraging federated learning, recommender systems can be trained on decentralized data sources without compromising user privacy. This approach allows models to be trained across multiple devices or servers while keeping user data secure and private.

Differential Privacy: Differential privacy techniques can be used to add noise to the training data or model parameters, ensuring that individual user data remains confidential. This can help protect against inference attacks and data leakage in recommender systems.

Secure Aggregation: Techniques like secure aggregation can be employed to protect the privacy of user data during the aggregation of model updates in collaborative filtering recommender systems.

Homomorphic Encryption: Homomorphic encryption can enable computations on encrypted data, allowing recommender systems to process user data without exposing sensitive information.

Model Robustness: Adversarial training techniques from machine learning can be applied to enhance the robustness of recommender systems against poisoning attacks by training models to resist adversarial manipulation.

Explainability and Transparency: Techniques for model explainability and transparency can help users understand how recommendations are generated, increasing trust in the system and reducing the impact of potential attacks.

By incorporating these techniques and principles from other fields, recommender systems can be designed to be more resilient, secure, and trustworthy, mitigating the risks associated with poisoning attacks and ensuring the integrity of the recommendations provided to users.