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Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

NeurIPS, 2021
Jamie Cui*, Chaochao Chen*, Lingjuan Lyu, Carl Yang*, Li Wang*

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing works assume that all data are available to the recommendation platform. However, in practice, user-item interacti…

Anti-Backdoor Learning: Training Clean Models on Poisoned Data

NeurIPS, 2021
Yige Li*, Xixiang Lyu*, Nodens Koren*, Lingjuan Lyu, Bo Li*, Xingjun Ma*

Backdoor attack has emerged as a major security threat to deep neural networks(DNNs). While existing defense methods have demonstrated promising results on detecting and erasing backdoor triggers, it is still not clear if measures can be taken to avoid the triggers from bein…

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

NeurIPS, 2021
Xu Xinyi*, Lingjuan Lyu, Xingjun Ma*, Chenglin Miao*, Chuan-Sheng Foo*, Bryan Kian Hsiang Low*

Collaborative machine learning provides a promising framework for different agents to pool their resources (e.g., data) for a common learning task. In realistic settings where agents are self-interested and not altruistic, they may be unwilling to share data or model without…

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