Graph neural networks (GNNs) are increasingly used to model complex patterns in graph-structured data. However, enabling them to ''forget'' designated information remains challenging, especially under privacy regulations such as the GDPR. Existing unlearning methods largely optimize for efficiency and scalability, yet they offer little transparency, and the black-box nature of GNNs makes it difficult to verify whether forgetting has truly occurred. We propose an explainability-driven verifier for GNN unlearning that snapshots the model before and verifies after deletion, using attribution shifts and localized structural changes (e.g., graph edit distance) as transparent evidence. The verifier uses five explainability metrics—residual attribution, heatmap shift, explainability score deviation, graph edit distance, and (diagnostic) graph rule shift. We evaluate two backbones (GCN, GAT) and four unlearning strategies (Retrain, GraphEditor, GNNDelete, IDEA) across five benchmarks (Cora, Citeseer, Pubmed, Coauthor?CS, Coauthor?Physics). Results show that Retrain and GNNDelete achieve near-complete forgetting, GraphEditor provides partial erasure, and IDEA leaves residual signals. These explanation deltas provide the primary, human-readable evidence of forgetting; we also report membership?inference ROC?AUC as a complementary, graph?wide privacy signal. The code is available at https://github.com/ImranAhsan23/F-E

Effective management of private keys is crucial to ensure the security and ownership of users’ data and digital assets in the Web3 environment. However, existing solutions often fail to adequately address private key management from the user’s perspective. Private key leakage and loss incidents occur frequently, resulting in significant losses of digital assets. Moreover, the conventional approach of revoking both the private and public keys after a leakage or loss accident is inconvenient in Web3, where the public key serves as the user’s wallet address or digital identity. To tackle the issue of user-side private key management in Web3, this paper presents KeyShield which is a leakage-and-loss-resilient private key protection scheme. KeyShield divides the user’s private key into three shares, securely stored across a primary device and a secondary device owned by the user, and a third storage module owned by the user or a semi-trusted service provider. For daily use of the private key, the user only needs to connect the primary and secondary devices. In the event of a leakage or loss, such as device theft or attack, an update process will be triggered to update the three shares, immediately invalidating the leaked or lost share while causing no changes to the public key. As a demonstration of KeyShield, we developed KeyShieldECC accessible on both Android and iOS platforms for managing Elliptic Curve Cryptography (ECC) private keys. The testing results show that for a 256-bit ECC private key, the daily use only needs 0.05 seconds and update needs 0.25 to 0.3 seconds on an ordinary smart phone.

The importance of a phrase within a document becomes most evident through its absence rather than its presence. Inspired by this observation, we redefine keyphrases as those whose removal most disrupts the document's meaning. Traditional unsupervised methods typically rely on document-level signals, such as term frequency or phrase-to document similarity, which overlook the contextual contribution of a phrase. This paper proposes CPSRank, an unsupervised keyphrase extraction method that evaluates the semantic importance of candidate phrases via a contextual perturbation score (CPS). The CPS quantifies the critical role of each phrase by combining contextual perturbation and content loss. CPSRank outperforms existing baselines in terms of F1 scores while providing deeper insights into the semantic value of keyphrases. We release our code at https://github.com/Splo2t/CPSRank.

Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign token embedding framework. Additionally, to provide a more structured foundation for sign language analysis, we introduce a dedicated notation system tailored for sign language that endeavors to encapsulate the nuanced gestures and movements that are integral with sign communication. The proposed ACE approach tracks a signer’s actions based on human posture estimation. Tokenizing these actions and capturing the token embedding using a short-time Fourier transform encapsulates the time-based behavioral changes. Hence, ACE offers input embedding to translate sign language into natural language sentences. When tested against a disaster sign language dataset using automated machine translation measures, ACE notably surpasses prior research in terms of translation capabilities, improving the performance by up to 5.79% for BLEU-4 and 5.46% for ROUGE-L metric.

With the development of ICT technology, users are actively participated in content creation and sharing. Service providers have provided more diverse and extensive information services. With the recent evolution to a customized service, the demand for the use of personal information is increasing. However, it affects the efficiency and convenience of service provision, as social problems and security threats such as personal information leakage, human trafficking, and misuse increase. In this article, we proposed an intelligent mediator-based enhanced smart contract for privacy protection. The proposed approach performs the tasks, which are mediation transactions, authorization, and transaction record management, with blockchain-based personal information management. Then it is possible to prevent misuse of personal information and to support rational decision-making on the transparency of information and the use of personal information by autonomously performing personal information management.

In this article, we address the problem of how to make the wireless device-to-device (D2D) video multicast systems have better quality provision with consideration of internet-of-things (IoT) applications. We propose an opportunistic transmission and fair resource allocation framework, including joint application-layer and physical-layer transmission and optimization. First, we use a parallel subchannels structure by concatenating the Fountain codes and diversity-embedded space-time block codes to provide reliable and flexible transmission in heterogeneous circumstances. Second, we exploit the quality of heterogeneous user experience (quality of experience) metric under D2D video multicast systems, with consideration of various channel states, device capability, video content urgency, and the number of demanding users. Third, we formulate reliable multiple video streams broadcasting to heterogeneous devices as an aggregate maximum utility achieving problem, and we use opportunistic scheduling to select suitable users in each transmission interval to improve the broadcasting utility. Fourth, we use the utility fair scheme to guide rate allocation among multicontent video multicast. Extensive performance comparison and analysis are presented to demonstrate efficiency of the proposed solution.

ABSTRACT As interest in social network studies has grown bigger along with the development of the Web, social network trust management and applications have come into the spotlight. The increasing interest in social network services that are open systems has motivated the need for a reliable trust model that enables practical information sharing and information protection. In this paper, we propose an identity management‐based social trust model for solving a sparsity problem and an information leakage. The proposed trust model contributes to increasing the opportunities for information sharing. In addition, the creation and use of identity groups with a clustering approach and partial identities in the proposed approach effectively address security and privacy risks in social networks. In experiments, the performance of the proposed approach is evaluated using precision–recall and F‐measures. Copyright © 2011 John Wiley & Sons, Ltd.