5 papers
Auditing Apple's DifferentialPrivacy.framework: Implementation Bugs, Misconfigurations, and Practical Risks
Rishav Chourasia, Ergute Bao, Uzair Javaid +1
Since 2016, Apple has claimed that device analytics collected to improve user experience are protected by differential privacy (DP). Apple's DifferentialPrivacy framework is deploy…
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
Jiayu Li, Zilong Zhao, Kevin Yee +2
Synthetic tabular data generation has gained significant attention for its potential in data augmentation and privacy-preserving data sharing. While recent methods like diffusion a…
Instruction Tuning of Large Language Models for Tabular Data Generation-in One Day
Milad Abdollahzadeh, Abdul Raheem, Zilong Zhao +5
Tabular instruction tuning has emerged as a promising research direction for improving LLMs understanding of tabular data. However, the majority of existing works only consider que…
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer
Jiayu Li, Bingyin Zhao, Zilong Zhao +3
Transformers have shown impressive results in tabular data generation. However, they lack domain-specific inductive biases which are critical for preserving the intrinsic character…
Laplace Transform Interpretation of Differential Privacy
Rishav Chourasia, Uzair Javaid, Biplap Sikdar
We introduce a set of useful expressions of Differential Privacy (DP) notions in terms of the Laplace transform of the privacy loss distribution. Its bare form expression appears i…