Related papers: Privacy-Preserving Filtering for Event Streams
Differential privacy provides strong privacy guarantees for machine learning applications. Much recent work has been focused on developing differentially private models, however there has been a gap in other stages of the machine learning…
Future autonomous vehicles will generate, collect, aggregate and consume significant volumes of data as key gateway devices in emerging Internet of Things scenarios. While vehicles are widely accepted as one of the most challenging mobility…
In this paper, we consider the problem of privacy preservation in the average consensus problem when communication among nodes is quantized. More specifically, we consider a setting where some nodes in the network are curious but not…
This paper considers the problem of releasing privacy-preserving load data of a decentralized operated power system. The paper focuses on data used to solve Optimal Power Flow (OPF) problems and proposes a distributed algorithm that…
Data sharing between different organizations is an essential process in today's connected world. However, recently there were many concerns about data sharing as sharing sensitive information can jeopardize users' privacy. To preserve the…
This paper is concerned with the security problem for interconnected systems, where each subsystem is required to detect local attacks using locally available information and the information received from its neighboring subsystems.…
The rapid growth of Internet of Things (IoT) devices has introduced significant challenges to privacy, particularly as network traffic analysis techniques evolve. While encryption protects data content, traffic attributes such as packet…
This paper presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundreds of attendees. We…
Many commonly used learning algorithms work by iteratively updating an intermediate solution using one or a few data points in each iteration. Analysis of differential privacy for such algorithms often involves ensuring privacy of each step…
Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed low-bandwidth user devices to estimate aggregate statistics. Two major challenges in this framework…
Most of the smart applications, such as smart energy metering devices, demand strong privacy preservation to strengthen data privacy. However, it is difficult to protect the privacy of the smart device data, especially on the client side.…
In this work, we consider a federated learning model in a wireless system with multiple base stations and inter-cell interference. We apply a differential private scheme to transmit information from users to their corresponding base station…
In settings like vaccination registries, individuals act after observing others, and the resulting public records can expose private information. We study privacy-preserving sequential learning, where agents add endogenous noise to their…
In recent years, differential privacy has emerged as the de facto standard for sharing statistics of datasets while limiting the disclosure of private information about the involved individuals. This is achieved by randomly perturbing the…
Recently, a novel class of incentive mechanisms is proposed to attract extensive users to truthfully participate in crowd sensing applications with a given budget constraint. The class mechanisms also bring good service quality for the…
Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to…
Data-driven systems are gathering increasing amounts of data from users, and sensitive user data requires privacy protections. In some cases, the data gathered is non-numerical or symbolic, and conventional approaches to privacy, e.g.,…
The integration of fairness and privacy in centralized data-driven applications is critical, especially as these systems increasingly influence sectors with significant societal impact. Current methods rarely address privacy, fairness, and…
Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting…
Privacy preservation is an important issue in today's context of extreme penetration of internet and mobile technologies. It is more important in the case of Wireless Sensor Networks (WSNs) where collected data often requires in-network…