237 citations · 723 across the 30 of their papers we have counts for
7 papers · 1 filter
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…
Privacy for Free: How does Dataset Condensation Help Privacy?
Tian Dong, Bo Zhao, Lingjuan Lyu
To prevent unintentional data leakage, research community has resorted to data generators that can produce differentially private data for model training. However, for the sake of…
DP-SIGNSGD: When Efficiency Meets Privacy and Robustness
Lingjuan Lyu
Federated learning (FL) has emerged as a promising collaboration paradigm by enabling a multitude of parties to construct a joint model without exposing their private training data…
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…
Local Differential Privacy based Federated Learning for Internet of Things
Yang Zhao, Jun Zhao, Mengmeng Yang +5
Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Tu…
Threats to Federated Learning: A Survey
Lingjuan Lyu, Han Yu, Qiang Yang
With the emergence of data silos and popular privacy awareness, the traditional centralized approach of training artificial intelligence (AI) models is facing strong challenges. Fe…
Towards Distributed Privacy-Preserving Prediction
Lingjuan Lyu, Yee Wei Law, Kee Siong Ng +4
In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or predic…