As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the ...

We present a privacy-preserving room occupancy estimation method using federated analytics of Bluetooth Low Energy (BLE) packets. By processing data locally and reporting only aggregated device counts, our approach preserves user privacy while achieving ...

Advanced deep learning-based face recognition models require extensive datasets for optimal performance. However, increasing privacy concerns drive the limitation of face image access on devices to prevent personal information leaks. To address this, ...

Enabling various parties to share data enhances online fraud detection capabilities considering fraudsters tend to reuse resources attacking multiple platforms. Multi-party computation (MPC) techniques, such as secret sharing, offer potential privacy-...

Considering data safety in more and more applied peer-to-peer networks, such as wireless sensor networks, has become the focus of information fusion, this paper proposes the problem of credible evidence fusion (CEF) in a distributed system with ...

• A distributed credible evidence fusion method for peer-to-peer networks.
• The privacy of raw evidence is protected.
• Evidence credibility is computed based on evidence comparison.
• Simultaneously conducting credibility estimation ...

Permissioned blockchain is often used in multi-consortium applications due to its unique features such as provenance, immutability, and tamper-resistance. For these applications working in a malicious environment, privacy protection is very ...

Federated learning involves training statistical models over edge devices such as mobile phones such that the training data are kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that rely on ...

Access to computing resources is a critical requirement for researchers in a wide diversity of areas. This has become even more important with the rise of artificial intelligence techniques through the training of machine learning and deep learning ...

Graph Neural Networks (GNNs) are increasingly used in fields like social media and bioinformatics, promoting the prosperity of cloud-based GNN inference services. Nevertheless, data privacy becomes a critical issue when handling sensitive information. ...

Our workshop aims to provide a platform for both academic and industrial professionals engaged in the analysis and retrieval of cross-data from diverse perspectives, with a particular emphasis on wearable and ambient sensors, lifelog cameras, social ...

Privacy-preserving content-based image retrieval aims to safeguard the security of outsourced private images while maintaining their searchability. However, existing schemes encounter challenges in striking a balance between security, accuracy, and ...

Insider threats and attacks have witnessed a concerning rise in recent years, posing significant risks and financial implications for businesses. Malicious insiders exploit memory dumping tools to leak data, resulting in a substantial surge in data ...

Recent studies have introduced privacy-preserving graph neural networks to safeguard the privacy of sensitive link information in graphs. However, existing link protection mechanisms in GNNs, particularly over decentralized nodes, struggle to strike an ...

The heterogeneous information network (HIN), which contains rich semantics depicted by meta-paths, has emerged as a potent tool for mitigating data sparsity in recommender systems. Existing HIN-based recommender systems operate under the assumption of ...

As people inevitably interact with items across multiple domains or various platforms, cross-domain recommendation (CDR) has gained increasing attention. However, the rising privacy concerns limit the practical applications of existing CDR models, since ...

In scientific environments, the frequent need to process substantial volumes of data poses a common challenge. Individuals tasked with executing these computations frequently encounter a deficit in local computational resources, leading them to opt for ...

Failure data collected from the field (e.g., failure traces, bug reports, and memory dumps) represent an invaluable source of information for developers who need to reproduce and analyze failures. Unfortunately, field data may include sensitive ...

Federated learning has emerged as a leading machine learning paradigm, promising to preserve data privacy while collaboratively training models. This approach is increasingly finding applications in the Internet of Things, particularly in the context of ...

Clustering is a significant unsupervised machine learning task widely used for data mining and analysis. Fully homomorphic encryption allows data owners to outsource privacy-preserving computations without interaction. In this paper, we propose a ...

Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared ...