Fake Co-visitation Injection Attacks to Recommender Systems

By Guolei Yang, Neil Zhenqiang Gong, Ying Cai. 2017

129 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research, Safety, Robustness & Interpretability, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Neil Zhenqiang Gong
2. Jinyuan Jia
3. Xiaoyu Cao
4. Minghong Fang
5. Guolei Yang
6. Jia Liu
7. Vale Tolpegin
8. Mehmet Emre Gursoy

In Information Systems

1. Neil Zhenqiang Gong
2. Jinyuan Jia
3. Xiaoyu Cao
4. Minghong Fang
5. Guolei Yang
6. Jia Liu
7. Binghui Wang
8. Ling Liu

In Management Science and Operations Research

1. Xiaoyu Cao
2. Neil Zhenqiang Gong
3. Guolei Yang
4. Jinyuan Jia
5. Ling Liu
6. Jia Liu

In Safety, Robustness & Interpretability

1. Neil Zhenqiang Gong
2. Jinyuan Jia
3. Xiaoyu Cao
4. Minghong Fang
5. Hongbin Liu
6. Vale Tolpegin
7. Mehmet Emre Gursoy
8. Stacey Truex

In Recommender Systems

1. Neil Zhenqiang Gong
2. Xiaoyu Cao
3. Guolei Yang

Citations in the corpus

• Local Model Poisoning Attacks to Byzantine-Robust Federated Learning (2019, background): “Attacker’s goal: Like many studies on poisoning attacks [7, 8, 30, 33, 50, 62, 65], we consider an attacker’s goal is to manipulate the learnt global model such that it has a high error rate indiscriminately for testing examples.”
• Poisoning Attacks to Graph-Based Recommender Systems (2018, background): “[35] proposed poisoning aacks optimized for association-rule-based recommender systems.”
• PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning (2022, background): “Examples include recommender systems [19, 21, 27, 36, 60], federated analytics [7, 9, 10, 18, 20, 39, 58], search engines [33], as well as natural language models [47, 48].”
• FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping (2020, unknown): “[17], [25], [45], neural networks [11], [18], [27], [30], [36], [37], and graph-based methods [20], [41], [49], as well as distributed privacy-preserving data analytics [10], [14].”
• Data Poisoning Attacks Against Federated Learning Systems (2020, background): “Poisoning attacks are highly relevant in domains such as spam filtering [10,32], malware and network anomaly detection [11,24,39], disease diagnosis [29], computer vision [34], and recommender systems [15,54].”
• Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification (2018, background): “Various studies have demonstrated poisoning attacks to spam filter [22], support vector machines [1], deep neural networks [26], and recommender systems [15, 30].”