Generative models for synthetic urban mobility data: A systematic literature review
A Kapp, J Hansmeyer, H Mihaljević - ACM Computing Surveys, 2023 - dl.acm.org
Although highly valuable for a variety of applications, urban mobility data are rarely made
openly available, as it contains sensitive personal information. Synthetic data aims to solve …
openly available, as it contains sensitive personal information. Synthetic data aims to solve …
The applicability of reinforcement learning methods in the development of industry 4.0 applications
Reinforcement learning (RL) methods can successfully solve complex optimization
problems. Our article gives a systematic overview of major types of RL methods, their …
problems. Our article gives a systematic overview of major types of RL methods, their …
Privacy-preserving aggregate mobility data release: An information-theoretic deep reinforcement learning approach
It is crucial to protect users' location traces against inference attacks on aggregate mobility
data collected from multiple users in various real-world applications. Most of the existing …
data collected from multiple users in various real-world applications. Most of the existing …
Privacy preserving large language models: Chatgpt case study based vision and framework
Abstract The generative Artificial Intelligence (AI) tools based on Large Language Models
(LLMs) use billions of parameters to extensively analyse large datasets and extract critical …
(LLMs) use billions of parameters to extensively analyse large datasets and extract critical …
Semantic-aware privacy-preserving online location trajectory data sharing
Although users can obtain various services by sharing their location information online with
location-based service providers, it reveals sensitive information about users. However …
location-based service providers, it reveals sensitive information about users. However …
Procurement 4.0 to the rescue: catalysing its adoption by modelling the challenges
JJ Joseph Jerome, D Saxena, V Sonwaney… - Benchmarking: An …, 2022 - emerald.com
Purpose The pandemic crisis has resulted in global chaos that had caused massive
disruption to the supply chain. The pharmaceutical industry, in particular, has been working …
disruption to the supply chain. The pharmaceutical industry, in particular, has been working …
An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages
The paper's state-of-the-art review focuses on an in-depth evaluation of smart home energy
management systems which employ reinforcement learning-based methods to integrate …
management systems which employ reinforcement learning-based methods to integrate …
Evaluating privacy-preserving machine learning in critical infrastructures: A case study on time-series classification
With the advent of machine learning in applications of critical infrastructure such as
healthcare and energy, privacy is a growing concern in the minds of stakeholders. It is …
healthcare and energy, privacy is a growing concern in the minds of stakeholders. It is …
adaparl: Adaptive privacy-aware reinforcement learning for sequential decision making human-in-the-loop systems
Reinforcement learning (RL) presents numerous benefits compared to rule-based
approaches in various applications. Privacy concerns have grown with the widespread use …
approaches in various applications. Privacy concerns have grown with the widespread use …
Privacy-aware communication over a wiretap channel with generative networks
We study privacy-aware communication over a wiretap channel using end-to-end learning.
Alice wants to transmit a source signal to Bob over a binary symmetric channel, while …
Alice wants to transmit a source signal to Bob over a binary symmetric channel, while …