相关论文: Hierarchical Identity-Based Lossy Trapdoor Functio…

Several identity based and implicitly authenticated key agreement protocols have been proposed in recent years and none of them has achieved all required security properties. In this paper, we propose an efficient identity-based and…

Physical human-robot interaction offers the potential to leverage human intelligence and robot physical capabilities to enable a range of exciting applications, e.g., collaborative robots for rehabilitation. Safety is critical for the…

The existing authentication system has two entry points (i.e., username and password fields) to interact with the outside, but neither of them has a gatekeeper, making the system vulnerable to cyberattacks. In order to ensure the…

Deep learning models are vulnerable to backdoor attacks, where adversaries inject malicious functionality during training that activates on trigger inputs at inference time. Extensive research has focused on developing stealthy backdoor…

Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational behavior of human groups. As hierarchy is introduced as a…

Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging…

Horizontal Federated Learning (HFL) is particularly vulnerable to backdoor attacks as adversaries can easily manipulate both the training data and processes to execute sophisticated attacks. In this work, we study the impact of training…

Federated Learning (FL) enables collaborative model training on decentralized data but remains vulnerable to gradient leakage attacks that can reconstruct sensitive user information. Existing defense mechanisms, such as differential privacy…

With the increased use of network technologies like Internet of Things (IoT) in many real-world applications, new types of cyberattacks have been emerging. To safeguard critical infrastructures from these emerging threats, it is crucial to…

Ciphertext-policy hierarchical attribute-based encryption (CP-HABE) is a promising cryptographic primitive for enforcing the fine-grained access control with scalable key delegation and user revocation mechanisms on the outsourced encrypted…

Federated Learning (FL) enables collaborative model training while preserving data privacy by keeping raw data locally stored on client devices, preventing access from other clients or the central server. However, recent studies reveal that…

Advancements in computer networks and communication technologies like software defined networks (SDN), Internet of things (IoT), microservices architecture, cloud computing and network function virtualization (NFV) have opened new fronts…

The Internet of Flying Things (IoFT) plays a vital role in modern applications such as aerial surveillance and smart mobility. However, it remains highly vulnerable to cyberattacks that threaten the confidentiality, integrity, and…

Tensor-valued data, increasingly common in distributed big data applications like autonomous driving and smart healthcare, poses unique challenges for privacy protection due to its multidimensional structure and the risk of losing critical…

This paper studies the problem of utilizing data-driven adaptive control techniques to guarantee stability and safety of uncertain nonlinear systems with high relative degree. We first introduce the notion of a High Order Robust Adaptive…

The increasing complexity of cryptographic extortion techniques has necessitated the development of adaptive detection frameworks capable of identifying adversarial encryption behaviors without reliance on predefined signatures.…

Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific…

"Distributed Identity" refers to the transition from centralized identity systems using Decentralized Identifiers (DID) and Verifiable Credentials (VC) for secure and privacy-preserving authentications. With distributed identity, control of…

Federated learning (FL), where data remains at the federated clients, and where only gradient updates are shared with a central aggregator, was assumed to be private. Recent work demonstrates that adversaries with gradient-level access can…

There has been a plethora of microarchitectural-level attacks leading to many proposed countermeasures. This has created an unexpected and unaddressed security issue where naive integration of those defenses can potentially lead to security…