research
∙
09/06/2023
ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
Data is a critical asset in AI, as high-quality datasets can significant...
share
research
∙
06/30/2023
Parameter Identification for Partial Differential Equations with Spatiotemporal Varying Coefficients
To comprehend complex systems with multiple states, it is imperative to ...
research
∙
05/10/2023
DPMLBench: Holistic Evaluation of Differentially Private Machine Learning
Differential privacy (DP), as a rigorous mathematical definition quantif...
research
∙
04/05/2023
PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information
Graph data is used in a wide range of applications, while analyzing grap...
research
∙
02/13/2023
LDPTrace: Locally Differentially Private Trajectory Synthesis
Trajectory data has the potential to greatly benefit a wide-range of rea...
research
∙
10/14/2021
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy
For protecting users' private data, local differential privacy (LDP) has...
research
∙
10/06/2021
Inference Attacks Against Graph Neural Networks
Graph is an important data representation ubiquitously existing in the r...
research
∙
06/24/2021
DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges
We summarize the experience of participating in two differential privacy...
research
∙
03/27/2021
Graph Unlearning
The right to be forgotten states that a data subject has the right to er...
research
∙
02/04/2021
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Inference attacks against Machine Learning (ML) models allow adversaries...
research
∙
12/30/2020
PrivSyn: Differentially Private Data Synthesis
In differential privacy (DP), a challenging problem is to generate synth...
research
∙
09/10/2020
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning
While being deployed in many critical applications as core components, m...
research
∙
05/24/2020
Continuous Release of Data Streams under both Centralized and Local Differential Privacy
In this paper, we study the problem of publishing a stream of real-value...
research
∙
05/05/2020
When Machine Unlearning Jeopardizes Privacy
The right to be forgotten states that a data owner has the right to eras...
research
∙
12/23/2017
LEPA: Incentivizing Long-term Privacy-preserving Data Aggregation in Crowdsensing
In this paper, we study the incentive mechanism design for real-time dat...
research
∙
11/02/2017
REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing
Incentive mechanism plays a critical role in privacy-aware crowdsensing....
research
∙
11/01/2017