CAPE: Context-Aware Private Embeddings for Private Language Learning

27 August 2021

Papers citing "CAPE: Context-Aware Private Embeddings for Private Language Learning"

9 / 9 papers shown

Title
Anti-adversarial Learning: Desensitizing Prompts for Large Language Models Xuan Li Zhe Yin Xiaodong Gu Beijun Shen AAML MU 60 0 0 25 Apr 2025
On the Vulnerability of Text Sanitization Meng Tong Kejiang Chen Xiaojian Yuang J. Liu W. Zhang Nenghai Yu Jie Zhang 52 0 0 22 Oct 2024
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection Xingyu Ma Xin Tian Lingxiang Wu Xuepeng Wang Xueming Tang Jinqiao Wang 44 0 0 24 Sep 2024
Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization Zhigang Kan Linbo Qiao Hao Yu Liwen Peng Yifu Gao Dongsheng Li 26 20 0 14 Jun 2023
Privacy-Preserving Prompt Tuning for Large Language Model Services Yansong Li Zhixing Tan Yang Liu SILM VLM 45 63 0 10 May 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 15 18 0 22 Jan 2023
You Are What You Write: Preserving Privacy in the Era of Large Language Models Richard Plant V. Giuffrida Dimitra Gkatzia PILM 17 19 0 20 Apr 2022
Debiasing Pre-trained Contextualised Embeddings Masahiro Kaneko Danushka Bollegala 212 138 0 23 Jan 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 281 1,812 0 14 Dec 2020