Related papers: A Lightweight Incentive-Based Privacy-Preserving S…
The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs,…
Recent smart grid advancements enable near-realtime reporting of electricity consumption, raising concerns about consumer privacy. Differential privacy (DP) has emerged as a viable privacy solution, where a calculated amount of noise is…
Participatory sensing is emerging as an innovative computing paradigm that targets the ubiquity of always-connected mobile phones and their sensing capabilities. In this context, a multitude of pioneering applications increasingly carry out…
We prove that the expected estimation error of non-intrusive load monitoring algorithms is lower bounded by the trace of the inverse of the cross-correlation matrix between the derivatives of the load profiles of the appliances. We use this…
Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such…
Benchmarking is an important measure for companies to investigate their performance and to increase efficiency. As companies usually are reluctant to provide their key performance indicators (KPIs) for public benchmarks, privacy-preserving…
Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of the green technology to meet the environmental…
Proper privacy protection in RFID systems is important. However, many of the schemes known are impractical, either because they use hash functions instead of the more hardware efficient symmetric encryption schemes as a efficient…
Addressing trust concerns in Smart Home (SH) systems is imperative due to the limited study on preservation approaches that focus on analyzing and evaluating privacy threats for effective risk management. While most research focuses…
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…
The rapid expansion of Artificial Intelligence is hindered by a fundamental friction in data markets: the value-privacy dilemma, where buyers cannot verify a dataset's utility without inspection, yet inspection may expose the data (Arrow's…
Large language models (LLMs) have demonstrated exceptional capabilities in text understanding and generation, and they are increasingly being utilized across various domains to enhance productivity. However, due to the high costs of…
The widespread integration of Artificial Intelligence of Things (AIoT) in smart home environments has amplified the demand for transparent and interpretable machine learning models. To foster user trust and comply with emerging regulatory…
Advances in sensing, networking, and actuation technologies have resulted in the IoT wave that is expected to revolutionize all aspects of modern society. This paper focuses on the new challenges of privacy that arise in IoT in the context…
In this work, we investigate if statistical privacy can enhance the performance of ORAM mechanisms while providing rigorous privacy guarantees. We propose a formal and rigorous framework for developing ORAM protocols with statistical…
The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, owing to the remarkable advances in computer vision and large…
Smart metering networks are increasingly susceptible to cyber threats, where false data injection (FDI) appears as a critical attack. Data-driven-based machine learning (ML) methods have shown immense benefits in detecting FDI attacks via…
Blockchain enables novel, trustworthy Process-Aware Information Systems (PAISs) by enforcing the security, robustness, and traceability of operations. In particular, transparency ensures that all information exchanges are openly accessible,…
Currently most progresses on quantum secret sharing suffer from rate-distance bound, and thus the key rates are limited. In addition to the limited key rate, the technical difficulty and the corresponding cost together prevent large-scale…
User-facing software services are becoming increasingly reliant on remote servers to host Deep Neural Network (DNN) models, which perform inference tasks for the clients. Such services require the client to send input data to the service…