Search Results for author:
Found 22 papers, 14 papers with code
SoK: Dataset Copyright Auditing in Machine Learning Systems
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.
$S^2$NeRF: Privacy-preserving Training Framework for NeRF
Neural Radiance Fields (NeRF) have revolutionized 3D computer vision and graphics, facilitating novel view synthesis and influencing sectors like extended reality and e-commerce.
Parameters Inference for Nonlinear Wave Equations with Markovian Switching
Traditional partial differential equations with constant coefficients often struggle to capture abrupt changes in real-world phenomena, leading to the development of variable coefficient PDEs and Markovian switching models.
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method
This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems.
DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training
DPAdapter modifies and enhances the sharpness-aware minimization (SAM) technique, utilizing a two-batch strategy to provide a more accurate perturbation estimate and an efficient gradient descent, thereby improving parameter robustness against noise.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
FAKEPCD: Fake Point Cloud Detection via Source Attribution
Take the open-world attribution as an example, FAKEPCD attributes point clouds to known sources with an accuracy of 0. 82-0. 98 and to unknown sources with an accuracy of 0. 73-1. 00.
The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks
In our research, we propose a novel attack, Janus, which exploits the fine-tuning interface to recover forgotten PIIs from the pre-training data in LLMs.
ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
In safety-critical domains such as autonomous vehicles, offline deep reinforcement learning (offline DRL) is frequently used to train models on pre-collected datasets, as opposed to training these models by interacting with the real-world environment as the online DRL.
LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors
Instead of directly inverting the triggers, LMSanitator aims to invert the predefined attack vectors (pretrained models' output when the input is embedded with triggers) of the task-agnostic backdoors, which achieves much better convergence performance and backdoor detection accuracy.
Parameter Identification for Partial Differential Equations with Spatiotemporal Varying Coefficients
To comprehend complex systems with multiple states, it is imperative to reveal the identity of these states by system outputs.
Generated Graph Detection
Graph generative models become increasingly effective for data distribution approximation and data augmentation.
DPMLBench: Holistic Evaluation of Differentially Private Machine Learning
We also explore some improvements that can maintain model utility and defend against MIAs more effectively.
FACE-AUDITOR: Data Auditing in Facial Recognition Systems
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.
On the Privacy Risks of Cell-Based NAS Architectures
Existing studies on neural architecture search (NAS) mainly focus on efficiently and effectively searching for network architectures with better performance.
Finding MNEMON: Reviving Memories of Node Embeddings
Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of graph neural networks.
Inference Attacks Against Graph Neural Networks
Second, given a subgraph of interest and the graph embedding, we can determine with high confidence that whether the subgraph is contained in the target graph.
Graph Unlearning
In this paper, we propose GraphEraser, a novel machine unlearning framework tailored to graph data.
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
As a result, we lack a comprehensive picture of the risks caused by the attacks, e. g., the different scenarios they can be applied to, the common factors that influence their performance, the relationship among them, or the effectiveness of possible defenses.
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning
One major privacy attack in this domain is membership inference, where an adversary aims to determine whether a target data sample is part of the training set of a target ML model.
Continuous Release of Data Streams under both Centralized and Local Differential Privacy
To our knowledge, this is the first LDP algorithm for publishing streaming data.
When Machine Unlearning Jeopardizes Privacy
More importantly, we show that our attack in multiple cases outperforms the classical membership inference attack on the original ML model, which indicates that machine unlearning can have counterproductive effects on privacy.
