With the widespread adoption of interactive, mobile, and wearable technologies, an increasing amount of privacy-sensitive and statistically heterogeneous sensor data is being continuously generated by ubiquitous user devices. Federated learning (FL) enables multiple parties to jointly train a machine-learning model whi...
Ming Jiang, Xu-Hao Ren, Xi-Xi Zheng et al.· IEEE Transactions on Network...· 0 citations
Blockchain oracles enable reliable data interaction between blockchains and the external world. However, existing oracles are unsuitable for blockchain-based IoV scenarios due to the characteristics of vehicle mobility and data locality, which may result in unreliable data uploaded to the blockchain. To this end, we pr...
Deng-Cheng Hu, Jian-Rong Wang, Yi-Wei Cai et al.· IEEE Transactions on Mobile...· 0 citations
Dynamic Searchable Symmetric Encryption (DSSE) enables efficient searches over encrypted data but often suffers from search pattern leakage, allowing adversaries to infer sensitive information. While schemes using Oblivious RAM (ORAM) mitigate leakage, their high cost hinders large-scale applications. Dory adopts a mul...
Man-Yue Hu, Cong Zuo, Shu-Jie Cui et al.· IEEE Transactions on Informa...· 0 citations
Edge devices handle sensitive user data, raising significant security and privacy concerns when shared with data collectors for downstream learning tasks. One potential solution leverages differentially private generative models, keeping original data on user devices and creating obfuscated variants for transmission. H...
Adil Sarwar, Yanlong Zhai, Jun Shen et al.· IEEE Transactions on Mobile...· 0 citations
Mobile edge computing (MEC) accelerates Internet of Things (IoT) applications by caching content near end users. However, cached data remains vulnerable to corruption, misleading applications, and eroding user trust. Traditional centralized edge data integrity verification (EDIV) methods adopt challenge-response protoc...
Adil Sarwar, Yanlong Zhai, Jun Shen et al.· IEEE Internet of Things Jour...· 0 citations
Federated learning (FL) enables distributed clients to collaboratively train machine learning models without sharing raw data, making it well-suited for edge computing environments. However, deploying FL at the edge introduces critical challenges, including potential model poisoning by malicious clients or edge servers...
Xiaodong Shen, Chang Xu, Liehuang Zhu et al.· IEEE Internet of Things Jour...· 0 citations
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