相关论文: Improving Privacy-Preserving Techniques for Smart …

Data driven approaches to problem solving are, in many regards, the holy grail of evidence backed decision making. Using first-party empirical data to analyze behavior and establish predictions yields us the ability to base in-depth…

The Internet of Things is progressively getting broader, evol-ving its scope while creating new markets and adding more to the existing ones. However, both generation and analysis of large amounts of data, which are integral to this…

With the booming of smart grid, The ubiquitously deployed smart meters constitutes an energy internet of things. This paper develops a novel blockchain-based transactive energy management system for IoT-aided smart homes. We consider a…

User-level differentially private stochastic convex optimization (DP-SCO) has garnered significant attention due to the paramount importance of safeguarding user privacy in modern large-scale machine learning applications. Current methods,…

Local differential privacy (LDP) enables the efficient release of aggregate statistics without having to trust the central server (aggregator), as in the central model of differential privacy, and simultaneously protects a client's…

Privacy is a major issue in learning from distributed data. Recently the cryptographic literature has provided several tools for this task. However, these tools either reduce the quality/accuracy of the learning algorithm---e.g., by adding…

Traceability and auditability are key structures that are vital in supply chain management and construction. However, trust is the most important aspect of customers in these systems. Also, we have to rely on third parties to trade in…

The smart meter (SM) privacy problem is addressed together with the cost of energy for the user. It is assumed that a storage device, e.g., an electrical battery, is available to the user, which can be utilized both to achieve privacy and…

This paper presents a holistic approach to gradient leakage resilient distributed Stochastic Gradient Descent (SGD). First, we analyze two types of strategies for privacy-enhanced federated learning: (i) gradient pruning with random…

Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data. However, it is hampered by the flaw that current approaches are forced to accept a compromise between performance and privacy:…

Smart power grids offer to revolutionize power distribution by sharing granular power usage data, though this same data sharing can reveal a great deal about users, and there are serious privacy concerns for customers. In this paper, we…

With rapid developments of information and technology, large scale network data are ubiquitous. In this work we develop a distributed spectral clustering algorithm for community detection in large scale networks. To handle the problem, we…

Third-party analysis on private records is becoming increasingly important due to the widespread data collection for various analysis purposes. However, the data in its original form often contains sensitive information about individuals,…

The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called…

Public key searchable encryption (PKSE) scheme allows data users to search over encrypted data. To identify illegal users, many traceable PKSE schemes have been proposed. However, existing schemes cannot trace the keywords which illegal…

Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients and data remains on their devices, only sharing the local model updates. With this feature, federated…

The intersection of cloud computing, blockchain technology, and the impending era of quantum computing presents a critical juncture for data security. This research addresses the escalating vulnerabilities by proposing a comprehensive…

Blockchain has been praised for its capacity to hold data in a decentralized and tamper-proof way. It also supports the execution of code through blockchain's smart contracts, adding automation of actions to the network with high…

Communication efficiency and privacy protection are two critical issues in distributed machine learning. Existing methods tackle these two issues separately and may have a high implementation complexity that constrains their application in…

The use of codes defined by sparse characteristic matrices, like QC-LDPC and QC-MDPC codes, has become an established solution to design secure and efficient code-based public-key encryption schemes, as also witnessed by the ongoing NIST…