相关论文: A Novel DID Method Leveraging the IOTA Tangle and …

The terms self-sovereign identity (SSI) and decentralised identity are often used interchangeably, which results in increasing ambiguity when solutions are being investigated and compared. This article aims to provide a clear distinction…

Single Sign-On (SSO) systems simplify login procedures by using an an Identity Provider (IdP) to issue authentication tokens which can be consumed by Service Providers (SPs). Traditionally, IdPs are modeled as trusted third parties. This is…

The Self-Sovereign Identity (SSI) paradigm is instrumental for decentralised identity management, allowing an entity to create, manage, and present their digital credentials without relying on centralised authorities. Credential selective…

Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT) are two approaches widely used in Semi-Supervised Learning (SSL) methods. These methods exhibit great power in many machine learning tasks by utilizing unlabeled…

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and…

Heterogeneous Internet of Things (IoT) systems suffer from fragmentation across hardware architectures, networking stacks, and data serialization formats. Existing standards (such as MQTT, COAP, and DDS) rely on address-bound, imperative…

Device-independent quantum key distribution (DIQKD) exploits the violation of a Bell inequality to extract secure key even if the users' devices are untrusted. Currently, all DIQKD protocols suffer from the secret key capacity bound, i.e.,…

Authentication and authorization are two key elements of a software application. In modern day, OAuth 2.0 framework and OpenID Connect protocol are widely adopted standards fulfilling these requirements. These protocols are implemented into…

Detecting Out-of-Distribution (OOD) inputs is crucial for improving the reliability of deep neural networks in the real-world deployment. In this paper, inspired by the inherent distribution shift between ID and OOD data, we propose a novel…

We propose Data Tumbling Layer (DTL), a cryptographic scheme for non-interactive data tumbling. The core concept is to enable users to commit to specific data and subsequently re-use to the encrypted version of these data across different…

The growing number of Internet users and the prevalence of web applications make it necessary to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems,…

The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of Internet of Thing (IoT), to develop new services and improve citizens' quality of life. Sensors and smart devices generate large…

Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which is distinct and complementary to existing works: Instead of…

A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency efficient. However, existing eye-based frameworks a) perform…

Existing digital identity management systems fail to deliver the desirable properties of control by the users of their own identity data, credibility of disclosed identity data, and network-level anonymity. The recently proposed…

The emergence of distributed ledger technology (DLT) based upon a blockchain data structure, has given rise to new approaches to identity management that aim to upend dominant approaches to providing and consuming digital identities. These…

Out-of-distribution (OOD) detection is the key to deploying models safely in the open world. For OOD detection, collecting sufficient in-distribution (ID) labeled data is usually more time-consuming and costly than unlabeled data. When ID…

We propose a new semi-supervised learning method on face-related tasks based on Multi-Task Learning (MTL) and data distillation. The proposed method exploits multiple datasets with different labels for different-but-related tasks such as…

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art…

Semi-Supervised Learning (SSL) with mismatched classes deals with the problem that the classes-of-interests in the limited labeled data is only a subset of the classes in massive unlabeled data. As a result, the classes only possessed by…