Publications related to Privacy Enhanced Demand Response with Reputation-based Incentive Distribution

Related publications (30)

From PV to EV: Mapping the Potential for Electric Vehicle Charging with Solar Energy in Europe

Christophe Ballif, Nicolas Würsch, Alejandro Pena Bello, Noémie Alice Yvonne Ségolène Jeannin, Jérémy Dumoulin

The electrification of the transport sector is a key element in decarbonizing our societies. However, energy systems will have to cope with additional electricity demand due to the charging needs of electric vehicles (EVs) together with the integration of ...

2024

Identification of typical district configurations: A two-step global sensitivity analysis framework

François Maréchal, Jonas Schnidrig, Cédric Terrier, Arthur Antoine Chuat

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Pergamon-Elsevier Science Ltd2024

Differentially private multi-agent constraint optimization

Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle

Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...

Springer2024

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Sayan Biswas

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Beyond the average consumer: Mapping the potential of demand-side management among patterns of appliance usage

Selin Yilmaz, Claudia Rebeca Binder Signer, Matteo Barsanti

To support the decarbonisation of the power sector and offset the volatility of a system with high levels of renewables, there is growing interest in residential Demand-Side Management (DSM) solutions. Traditional DSM strategies require consumers to active ...

2024

The Privacy Power of Correlated Noise in Decentralized Learning

Rachid Guerraoui, Martin Jaggi, Youssef Allouah, Anastasiia Koloskova

Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

PMLR2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Touradj Ebrahimi, Lin Yuan, Xiao Pu

The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Renewable energy integration and waste heat valorization in aluminum remelting for co-producing kerosene and methanol

François Maréchal, Daniel Alexander Florez Orrego, Meire Ellen Gorete Ribeiro Domingos

The aluminium sector relies on natural gas for the conversion of recycled scrap into new feedstock, which results in substantial atmospheric emissions. Hydric resources are also impacted, as they serve as heat sinks for the waste heat generated during the ...

Associazione Italiana Di Ingegneria Chimica2024

The thermodynamic and life-cycle assessments of a novel charging station for electric vehicles in dynamic and steady-state conditions

Jan Van Herle, Hossein Pourrahmani

The current study performs the thermodynamic and life-cycle assessments (LCA) of a novel charging station in two system designs. The goal is to design an efficient charging station for electric vehicles with high efficiencies and low environmental impacts ...

Nature Portfolio2023

Lab-Scale Investigation of the Integrated Backup/Storage System for Wind Turbines Using Alkaline Electrolyzer

Jan Van Herle, Hossein Pourrahmani

The depletion of fossil fuel sources has encouraged the authorities to use renewable resources such as wind energy to generate electricity. A backup/storage system can improve the performance of wind turbines, due to fluctuations in power demand. The novel ...

MDPI2023

Identification of typical district configurations: A two-step global sensitivity analysis framework

François Maréchal, Jonas Schnidrig, Cédric Terrier

The recent geopolitical conflicts in Europe highlighted the sensibility of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy system configurations to ensure energy supply stability, it is necess ...

2023

Heat pumping and renewable energy integration for decarbonizing brewery industry and district heating

François Maréchal, Daniel Alexander Florez Orrego, Meire Ellen Gorete Ribeiro Domingos

The energy integration of brewery plants and urban agglomerations has many benefits in terms of rational energy use and reduced environmental impact, especially during scenarios of uncertain supply chain and volatile market prices. In fact, the diversifica ...

Elsevier2023

Leveraging industrial biorefineries for the energy transition

François Maréchal, Julia Granacher

Biorefineries hold the potential to provide products and energy carriers at reduced environmental impact compared to their fossil-based counterparts. Thus, they can contribute to the decarbonization of sectors in which electrification of demands is challen ...

Elsevier Sci Ltd2023

Privacy-Preserving Machine Learning on Graphs

Sina Sajadmanesh

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

EPFL2023

Renewable Energy Integration and Waste Heat Recovery for the Production of Sustainable Jet Fuel and Decarbonization of Industrial Heating Applications

François Maréchal, Daniel Alexander Florez Orrego, Meire Ellen Gorete Ribeiro Domingos

The process synthesis and optimization methods used in this work include simulation of biomass gasification, hydrogen and synthetic natural gas production, and jet fuel production via a Fisher Tropsch unit. An energy integration approach, carried out by th ...

American Institute of Chemical Engineering2023

Photonic radar for contactless vital sign detection

Yang Liu

Vital sign detection is used across ubiquitous scenarios in medical and health settings, and contact and wearable sensors have been widely deployed. However, they are unsuitable for patients with burn wounds or infants with insufficient areas for attachmen ...

Nature Publishing Group2023

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

Francesco Intoci, Apostolos Pyrgelis

Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., ne ...

IEEE2023

AI-based forecasting for optimised solar energy management and smart grid efficiency

Mostafa Nick, Amin Kaboli

This paper considers two pertinent research inquiries: 'Can an AI-based predictive framework be utilised for the optimisation of solar energy management?' and 'What are the ways in which the AI-based predictive framework can be integrated within the Smart ...

Taylor & Francis Ltd2023

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023