26 citations · 43 across the 8 of their papers we have counts for
8 papers
Integer Subspace Differential Privacy
Prathamesh Dharangutte, Jie Gao, Ruobin Gong +1
We propose new differential privacy solutions for when external \emph{invariants} and \emph{integer} constraints are simultaneously enforced on the data product. These requirements…
Optimal Local Bayesian Differential Privacy over Markov Chains
Darshan Chakrabarti, Jie Gao, Aditya Saraf +2
In the literature of data privacy, differential privacy is the most popular model. An algorithm is differentially private if its outputs with and without any individual's data are…
Subspace Differential Privacy
Jie Gao, Ruobin Gong, Fang-Yi Yu
Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized da…
Physics-informed generative neural network: an application to troposphere temperature prediction
Zhihao Chen, Jie Gao, Weikai Wang +1
The troposphere is one of the atmospheric layers where most weather phenomena occur. Temperature variations in the troposphere, especially at 500 hPa, a typical level of the middle…
Influencers and the Giant Component: the Fundamental Hardness in Privacy Protection for Socially Contagious Attributes
Aria Rezaei, Jie Gao, Anand D. Sarwate
The presence of correlation is known to make privacy protection more difficult. We investigate the privacy of socially contagious attributes on a network of individuals, where each…
On Privacy of Socially Contagious Attributes
Aria Rezaei, Jie Gao
A commonly used method to protect user privacy in data collection is to perform randomized perturbation on user's real data before collection so that aggregated statistics can stil…
Application-driven Privacy-preserving Data Publishing with Correlated Attributes
Aria Rezaei, Chaowei Xiao, Jie Gao +2
Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of…
Performing Co-Membership Attacks Against Deep Generative Models
Kin Sum Liu, Chaowei Xiao, Bo Li +1
In this paper we propose a new membership attack method called co-membership attacks against deep generative models including Variational Autoencoders (VAEs) and Generative Adversa…