175 citations · 562 across the 37 of their papers we have counts for
26 papers · 1 filter
KUDA: Knowledge Unlearning by Deviating Representation for Large Language Models
Ce Fang, Zhikun Zhang, Min Chen +4
Large language models (LLMs) acquire a large amount of knowledge through pre-training on vast and diverse corpora. While this endows LLMs with strong capabilities in generation and…
DWBench: Holistic Evaluation of Watermark for Dataset Copyright Auditing
Xiao Ren, Xinyi Yu, Linkang Du +5
The surging demand for large-scale datasets in deep learning has heightened the need for effective copyright protection, given the risks of unauthorized use to data owners. Althoug…
PrivATE: Differentially Private Average Treatment Effect Estimation for Observational Data
Quan Yuan, Xiaochen Li, Linkang Du +6
Causal inference plays a crucial role in scientific research across multiple disciplines. Estimating causal effects, particularly the average treatment effect (ATE), from observati…
VICTOR: Dataset Copyright Auditing in Video Recognition Systems
Quan Yuan, Zhikun Zhang, Linkang Du +5
Video recognition systems are increasingly being deployed in daily life, such as content recommendation and security monitoring. To enhance video recognition development, many inst…
GradEscape: A Gradient-Based Evader Against AI-Generated Text Detectors
Wenlong Meng, Shuguo Fan, Chengkun Wei +5
In this paper, we introduce GradEscape, the first gradient-based evader designed to attack AI-generated text (AIGT) detectors. GradEscape overcomes the undifferentiable computation…
Bilateral Differentially Private Vertical Federated Boosted Decision Trees
Bokang Zhang, Zhikun Zhang, Haodong Jiang +5
Federated learning is a distributed machine learning paradigm that enables collaborative training across multiple parties while ensuring data privacy. Gradient Boosting Decision Tr…
CIBPU: A Conflict-Invisible Secure Branch Prediction Unit
Zhe Zhou, Fei Tong, Hongyu Wang +4
Previous schemes for designing secure branch prediction unit (SBPU) based on physical isolation can only offer limited security and significantly affect BPU's prediction capability…
PSGraph: Differentially Private Streaming Graph Synthesis by Considering Temporal Dynamics
Quan Yuan, Zhikun Zhang, Linkang Du +6
Streaming graphs are ubiquitous in daily life, such as evolving social networks and dynamic communication systems. Due to the sensitive information contained in the graph, directly…
SoK: Dataset Copyright Auditing in Machine Learning Systems
Linkang Du, Xuanru Zhou, Min Chen +5
As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data.…
NeRF: Privacy-preserving Training Framework for NeRF
Bokang Zhang, Yanglin Zhang, Zhikun Zhang +3
Neural Radiance Fields (NeRF) have revolutionized 3D computer vision and graphics, facilitating novel view synthesis and influencing sectors like extended reality and e-commerce. H…
FAKEPCD: Fake Point Cloud Detection via Source Attribution
Yiting Qu, Zhikun Zhang, Yun Shen +2
To prevent the mischievous use of synthetic (fake) point clouds produced by generative models, we pioneer the study of detecting point cloud authenticity and attributing them to th…
The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks
Xiaoyi Chen, Siyuan Tang, Rui Zhu +7
The rapid advancements of large language models (LLMs) have raised public concerns about the privacy leakage of personally identifiable information (PII) within their extensive tra…
InferDPT: Privacy-Preserving Inference for Closed-box Large Language Model
Meng Tong, Kejiang Chen, Jie Zhang +5
Large language models (LLMs), like ChatGPT, have greatly simplified text generation tasks. However, they have also raised concerns about privacy risks such as data leakage and unau…
ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
Linkang Du, Min Chen, Mingyang Sun +4
Data is a critical asset in AI, as high-quality datasets can significantly improve the performance of machine learning models. In safety-critical domains such as autonomous vehicle…
Generated Graph Detection
Yihan Ma, Zhikun Zhang, Ning Yu +4
Graph generative models become increasingly effective for data distribution approximation and data augmentation. While they have aroused public concerns about their malicious misus…
FACE-AUDITOR: Data Auditing in Facial Recognition Systems
Min Chen, Zhikun Zhang, Tianhao Wang +2
Few-shot-based facial recognition systems have gained increasing attention due to their scalability and ability to work with a few face images during the model deployment phase. Ho…
PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information
Quan Yuan, Zhikun Zhang, Linkang Du +3
Graph data is used in a wide range of applications, while analyzing graph data without protection is prone to privacy breach risks. To mitigate the privacy risks, we resort to the…
PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model
Haiming Wang, Zhikun Zhang, Tianhao Wang +4
Publishing trajectory data (individual's movement information) is very useful, but it also raises privacy concerns. To handle the privacy concern, in this paper, we apply different…
On the Privacy Risks of Cell-Based NAS Architectures
Hai Huang, Zhikun Zhang, Yun Shen +3
Existing studies on neural architecture search (NAS) mainly focus on efficiently and effectively searching for network architectures with better performance. Little progress has be…
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy
Linkang Du, Zhikun Zhang, Shaojie Bai +4
For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis.…
DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges
Ninghui Li, Zhikun Zhang, Tianhao Wang
We summarize the experience of participating in two differential privacy competitions organized by the National Institute of Standards and Technology (NIST). In this paper, we docu…
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Yugeng Liu, Rui Wen, Xinlei He +6
Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied,…
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning
Yang Zou, Zhikun Zhang, Michael Backes +1
While being deployed in many critical applications as core components, machine learning (ML) models are vulnerable to various security and privacy attacks. One major privacy attack…
Continuous Release of Data Streams under both Centralized and Local Differential Privacy
Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang +5
In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be q…