17 citations · 47 across the 11 of their papers we have counts for
10 papers · 1 filter
TabularMark: Watermarking Tabular Datasets for Machine Learning
Yihao Zheng, Haocheng Xia, Junyuan Pang +5
Watermarking is broadly utilized to protect ownership of shared data while preserving data utility. However, existing watermarking methods for tabular datasets fall short on the de…
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
Shun Takagi, Li Xiong, Fumiyuki Kato +2
Human mobility data offers valuable insights for many applications such as urban planning and pandemic response, but its use also raises privacy concerns. In this paper, we introdu…
PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps
Ruixuan Liu, Tianhao Wang, Yang Cao +1
The pre-training and fine-tuning paradigm has demonstrated its effectiveness and has become the standard approach for tailoring language models to various tasks. Currently, communi…
CARGO: Crypto-Assisted Differentially Private Triangle Counting without Trusted Servers
Shang Liu, Yang Cao, Takao Murakami +2
Differentially private triangle counting in graphs is essential for analyzing connection patterns and calculating clustering coefficients while protecting sensitive individual info…
Preventing Manipulation Attack in Local Differential Privacy using Verifiable Randomization Mechanism
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
Several randomization mechanisms for local differential privacy (LDP) (e.g., randomized response) are well-studied to improve the utility. However, recent studies show that LDP is…
PGLP: Customizable and Rigorous Location Privacy through Policy Graph
Yang Cao, Yonghui Xiao, Shun Takagi +6
Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off b…