Zhikun Zhang | DeepAI

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167 publications
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∙ 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...

0 Linkang Du, et al. ∙

∙ 06/30/2023

Parameter Identification for Partial Differential Equations with Spatiotemporal Varying Coefficients

To comprehend complex systems with multiple states, it is imperative to ...

0 Guangtao Zhang, et al. ∙

∙ 05/10/2023

DPMLBench: Holistic Evaluation of Differentially Private Machine Learning

Differential privacy (DP), as a rigorous mathematical definition quantif...

2 Chengkun Wei, et al. ∙

∙ 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...

0 Quan Yuan, et al. ∙

∙ 02/13/2023

LDPTrace: Locally Differentially Private Trajectory Synthesis

Trajectory data has the potential to greatly benefit a wide-range of rea...

0 Yuntao Du, et al. ∙

∙ 10/14/2021

AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy

For protecting users' private data, local differential privacy (LDP) has...

0 Linkang Du, et al. ∙

∙ 10/06/2021

Inference Attacks Against Graph Neural Networks

Graph is an important data representation ubiquitously existing in the r...

45 Zhikun Zhang, et al. ∙

∙ 06/24/2021

DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges

We summarize the experience of participating in two differential privacy...

0 Ninghui Li, et al. ∙

∙ 03/27/2021

Graph Unlearning

The right to be forgotten states that a data subject has the right to er...

23 Min Chen, et al. ∙

∙ 02/04/2021

ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models

Inference attacks against Machine Learning (ML) models allow adversaries...

0 Yugeng Liu, et al. ∙

∙ 12/30/2020

PrivSyn: Differentially Private Data Synthesis

In differential privacy (DP), a challenging problem is to generate synth...

0 Zhikun Zhang, et al. ∙

∙ 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...

7 Yang Zou, et al. ∙

∙ 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...

0 Tianhao Wang, et al. ∙

∙ 05/05/2020

When Machine Unlearning Jeopardizes Privacy

The right to be forgotten states that a data owner has the right to eras...

10 Min Chen, et al. ∙

∙ 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...

0 Zhikun Zhang, et al. ∙

∙ 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....

0 Zhikun Zhang, et al. ∙

∙ 11/01/2017

Re-DPoctor: Real-time health data releasing with w-day differential privacy

Wearable devices enable users to collect health data and share them with...

0 Jiajun Zhang, et al. ∙

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