623 citations · 2.1k across the 79 of their papers we have counts for
5 papers · 1 filter
FedDefender: Client-Side Attack-Tolerant Federated Learning
Sungwon Park, Sungwon Han, Fangzhao Wu +4
Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning a…
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
Xuanli He, Qiongkai Xu, Yi Zeng +4
Previous works have validated that text generation APIs can be stolen through imitation attacks, causing IP violations. In order to protect the IP of text generation APIs, a recent…
Robust Quantity-Aware Aggregation for Federated Learning
Jingwei Yi, Fangzhao Wu, Huishuai Zhang +4
Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning fram…
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Xuanli He, Qiongkai Xu, Lingjuan Lyu +2
Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulat…
Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs
Chen Chen, Xuanli He, Lingjuan Lyu +1
The collection and availability of big data, combined with advances in pre-trained models (e.g., BERT, XLNET, etc), have revolutionized the predictive performance of modern natural…