Related papers: Efficient User-Centric Privacy-Friendly and Flexib…
The proliferation of connected devices through Internet connectivity presents both opportunities for smart applications and risks to security and privacy. It is vital to proactively address these concerns to fully leverage the potential of…
Outsourcing data storage to the remote cloud can be an economical solution to enhance data management in the smart grid ecosystem. To protect the privacy of data, the utility company may choose to encrypt the data before uploading them to…
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…
Wearable devices like smartwatches, wristbands, and fitness trackers are designed to be lightweight devices to be worn on the human body. With the increased connectivity of wearable devices, they will become integral to remote healthcare…
Personal data custodian services enable data owners to share their data with data consumers in a convenient manner, anytime and anywhere. However, with data hosted in these services being beyond the control of the data owners, it raises…
Asynchronous federated learning (AFL) is an effective method to address the challenge of device heterogeneity in cross-device federated learning. However, AFL is usually incompatible with existing secure aggregation protocols used to…
Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home, smart building, to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart…
In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications usually is…
Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices. On-device computations combined with other privacy and…
Smart cities rely on dynamic and real-time data to enable smart urban applications such as intelligent transport and epidemics detection. However, the streaming of big data from IoT devices, especially from mobile platforms like pedestrians…
With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great…
In this paper, we present a multidimensional, highly effective method for aggregating data for wireless sensor networks while maintaining privacy. The suggested system is resistant to data loss and secure against both active and passive…
Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems being designed according to the concept of stream processing. A common area of application is the…
Healthcare data contains sensitive information, and it is challenging to persuade healthcare data owners to share their information for research purposes without any privacy assurance. The proposed hybrid medical data privacy protection…
Smart meter data aggregation protocols have been developed to address rising privacy threats against customers' consumption data. However, these protocols do not work satisfactorily in the presence of failures of smart meters or network…
Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of solutions are being explored but have not yet won the public's…
Privacy-preserving biometric authentication (PPBA) enables client authentication without revealing sensitive biometric data, addressing privacy and security concerns. Many studies have proposed efficient cryptographic solutions to this…
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private algorithms require little trust but are (provably) limited…
The Internet of Things (IoT) has become increasingly popular in people's daily lives. The pervasive IoT devices are encouraged to share data with each other in order to better serve the users. However, users are reluctant to share sensitive…
A smart grid is an advanced method for supplying electricity to the consumers alleviating the limitations of the existing system. It causes frequent meter reading transmission from the end-user to the supplier. This frequent data…