Related papers: Protecting User Privacy Based on Secret Sharing wi…
As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However,interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this…
In order to remain competitive, Internet companies collect and analyse user data for the purpose of improving user experiences. Frequency estimation is a widely used statistical tool which could potentially conflict with the relevant…
Cooperative spectrum sensing, despite its effectiveness in enabling dynamic spectrum access, suffers from location privacy threats, merely because secondary users (SUs)' sensing reports that need to be shared with a fusion center to make…
The growing popularity of e-mobility, heat pumps, and renewable generation such as photovoltaics is leading to scenarios which the distribution grid was not originally designed for. Moreover, parts of the distribution grid are only sparsely…
The smart grid is envisioned to significantly enhance the efficiency of energy consumption, by utilizing two-way communication channels between consumers and operators. For example, operators can opportunistically leverage the delay…
Stakeholders in electricity delivery infrastructure are amassing data about their system demand, use, and operations. Still, they are reluctant to share them, as even sharing aggregated or anonymized electric grid data risks the disclosure…
Smart meters are increasingly used worldwide. Smart meters are the advanced meters capable of measuring energy consumption at a fine-grained time interval, e.g., every 15 minutes. Smart meter data are typically bundled with social economic…
We present a data storage scheme for sensor networks that achieves the targets of encryption and distributed storage simultaneously. We partition the data to be stored into numerous pieces such that at least a specific number of them have…
Modern distributed applications in healthcare, supply chain, and the Internet of Things handle a large amount of data in a diverse application setting with multiple stakeholders. Such applications leverage advanced artificial intelligence…
With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit…
Decentralized methods are gaining popularity for data-driven models in power systems as they offer significant computational scalability while guaranteeing full data ownership by utility stakeholders. However, decentralized methods still…
Multi-access Edge Computing (MEC), an enhancement of 5G, processes data closer to its generation point, reducing latency and network load. However, the distributed and edge-based nature of 5G-MEC presents privacy and security challenges,…
Privacy-preserving smart meter control strategies proposed in the literature so far make some ideal assumptions such as instantaneous control without delay, lossless energy storage systems etc. In this paper, we present a one-step-ahead…
Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods. The idea is to compute a global weight update without revealing the…
Perfect data privacy seems to be in fundamental opposition to the economical and scientific opportunities associated with extensive data exchange. Defying this intuition, this paper develops a framework that allows the disclosure of…
This paper presents an architecture of a Personal Information Management System, in which individuals can define the access to their personal data by means of smart contracts. These smart contracts, running on the Ethereum blockchain,…
Advances in sensing and communication capabilities as well as power industry deregulation are driving the need for distributed state estimation in the smart grid at the level of the regional transmission organizations (RTOs). This leads to…
In this paper, we investigate the distributed load frequency control problem in a multi-area smart grid under external load disturbances and measurement noise. The novelty lies in that the information privacy is fully taken into account,…
Deep neural networks are increasingly being used in a variety of machine learning applications applied to rich user data on the cloud. However, this approach introduces a number of privacy and efficiency challenges, as the cloud operator…
Smart building management systems rely on sensors to optimize the operation of buildings. If an unauthorized user gains access to these sensors, a privacy leak may occur. This paper considers such a potential leak of privacy in a smart…