Privacy attacks against deep learning models and their countermeasures
Recently, deep learning is considered an important concept that is used in a lot of important
applications, which require accurate models, such as image classification, identification of …
applications, which require accurate models, such as image classification, identification of …
You are what you write: Preserving privacy in the era of large language models
Large scale adoption of large language models has introduced a new era of convenient
knowledge transfer for a slew of natural language processing tasks. However, these models …
knowledge transfer for a slew of natural language processing tasks. However, these models …
Balancing privacy and utility of spatio-temporal data for taxi-demand prediction
The growing demand for ride-hailing services has led to an increasing need for accurate taxi
demand prediction. However, the use of real passenger data to train predictive models …
demand prediction. However, the use of real passenger data to train predictive models …
Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation
As location-based services (LBS) have grown in popularity, more human mobility data has
been collected. The collected data can be used to build machine learning (ML) models for …
been collected. The collected data can be used to build machine learning (ML) models for …
A mapping study on privacy attacks in big data and iot
Application domains like big data and IoT require a lot of user data collected and analyzed
to extract useful information, and those data might include user's sensitive and personal …
to extract useful information, and those data might include user's sensitive and personal …
[PDF][PDF] A survey on membership inference attacks against machine learning
Y Bai, T Chen, M Fan - management, 2021 - ijns.jalaxy.com.tw
Nowadays, machine learning is widely used in various applications. However, machine
learning models are vulnerable to various membership inference attacks (MIAs) that leak …
learning models are vulnerable to various membership inference attacks (MIAs) that leak …
STM-A Privacy-Enhanced Solution for Spatio-Temporal Trajectory Management
In this demonstration paper, we present STM: a new system for securing and management
of vehicle trajectory data using a generative model that balances privacy and utility. For …
of vehicle trajectory data using a generative model that balances privacy and utility. For …
White-box Inference Attacks against Centralized Machine Learning and Federated Learning
J Ge - arXiv preprint arXiv:2301.03595, 2022 - arxiv.org
With the development of information science and technology, various industries have
generated massive amounts of data, and machine learning is widely used in the analysis of …
generated massive amounts of data, and machine learning is widely used in the analysis of …
SGTP: A Spatiotemporal Generalized Trajectory Publishing Method With Differential Privacy
S Qiu, D Pi, Y Wang, T Xu - Journal of Ambient Intelligence and …, 2023 - Springer
With the rapid development of location-based service technology, the leakage of trajectory
privacy has become more and more serious. In order to solve the problems of insufficient …
privacy has become more and more serious. In order to solve the problems of insufficient …
A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data
Location data is frequently collected from populations and shared in aggregate form to guide
policy and decision making. However, the prevalence of aggregated data also raises the …
policy and decision making. However, the prevalence of aggregated data also raises the …