Membership inference attacks on machine learning: A survey
Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …
image classification, text generation, audio recognition, and graph data analysis. However …
Anonymization techniques for privacy preserving data publishing: A comprehensive survey
A Majeed, S Lee - IEEE access, 2020 - ieeexplore.ieee.org
Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …
owners such as hospitals, banks, social network (SN) service providers, and insurance …
Reducing write amplification in flash by death-time prediction of logical block addresses
C Chakraborttii, H Litz - Proceedings of the 14th ACM International …, 2021 - dl.acm.org
Flash-based solid state drives lack support for in-place updates, and hence deploy a flash
translation layer to absorb the writes. For this purpose, SSDs implement a log-structured …
translation layer to absorb the writes. For this purpose, SSDs implement a log-structured …
[HTML][HTML] A survey on membership inference attacks and defenses in Machine Learning
Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …
handle graph-structured data and the improvement in practical applications. However, many …
Invernet: An inversion attack framework to infer fine-tuning datasets through word embeddings
Word embedding aims to learn the dense representation of words and has become a
regular input preparation in many NLP tasks. Due to the data and computation intensive …
regular input preparation in many NLP tasks. Due to the data and computation intensive …
FaceLeaks: Inference attacks against transfer learning models via black-box queries
SP Liew, T Takahashi - arXiv preprint arXiv:2010.14023, 2020 - arxiv.org
Transfer learning is a useful machine learning framework that allows one to build task-
specific models (student models) without significantly incurring training costs using a single …
specific models (student models) without significantly incurring training costs using a single …
Earning extra performance from restrictive feedbacks
Many machine learning applications encounter situations where model providers are
required to further refine the previously trained model so as to gratify the specific need of …
required to further refine the previously trained model so as to gratify the specific need of …
[PDF][PDF] 机器学习中成员推理攻击和防御研究综述
牛俊， 马骁骥， 陈颖， 张歌， 何志鹏， 侯哲贤… - Journal of Cyber …, 2022 - jcs.iie.ac.cn
摘要机器学习被广泛应用于各个领域, 已成为推动各行业革命的强大动力,
极大促进了人工智能的繁荣与发展. 同时, 机器学习模型的训练和预测均需要大量数据 …
极大促进了人工智能的繁荣与发展. 同时, 机器学习模型的训练和预测均需要大量数据 …
How Train–Test Leakage Affects Zero-Shot Retrieval
Neural retrieval models are often trained on (subsets of) the millions of queries of the MS
MARCO/ORCAS datasets and then tested on the 250 Robust04 queries or other TREC …
MARCO/ORCAS datasets and then tested on the 250 Robust04 queries or other TREC …