Results 191 to 200 of about 4,825 (248)

A cross-dataset harmonized intrusion detection framework with statistically validated multi-model learning. [PDF]

PLoS One
Mishra S, Alshammari NS, Hussain H, Alfahidah RA.   +3 more
europepmc   +1 more source

Health Information Exchange: Contributions from 2023. [PDF]

Yearb Med Inform
Bloomrosen M, Feldman SS.
europepmc   +1 more source

The Untapped Potential of Ascon Hash Functions: Benchmarking, Hardware Profiling, and Application Insights for Secure IoT and Blockchain Systems. [PDF]

Sensors (Basel)
Gladis Kurian M, Chen Y.
europepmc   +1 more source

A zero-shot prompt learning approach on fine-grained text classification. [PDF]

Sci Rep
Zhang Z, Ge L, Hu R, Wang Y.
europepmc   +1 more source

Research priorities for improved pandemic and epidemic intelligence.

Lancet Glob Health
Tornimbene B, Redies I, AbdelMalik P, Abernethy J, Anton N, Awofisayo-Okuyelu AA, Fitzner J, Franz C, Ghozzi S, Golding J, Haouala A, Hersey S, Imai-Eaton N, Kampmann B, Krause G, Lais RS, de Waroux OLP, Carrera RM, Ngamala PN, Norton A, Ross AL, Sangameshwaran R, Van der Zee M, Wymer E, Morgan O.   +24 more
europepmc   +1 more source

CLOUD SECURITY WITH DEDUPLICATION APPROACH

openaire   +1 more source

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FuzzyDedup: Secure Fuzzy Deduplication for Cloud Storage

IEEE Transactions on Dependable and Secure Computing, 2023
Tao Jiang, Xu Yuan, Ke Cheng
exaly   +2 more sources

S2Dedup

Proceedings of the 14th ACM International Conference on Systems and Storage, 2021
Secure deduplication allows removing duplicate content at third-party storage services while preserving the privacy of users' data. However, current solutions are built with strict designs that cannot be adapted to storage service and applications with different security and performance requirements. We present S2Dedup, a trusted hardware-based privacy-
Mariana Miranda, Tânia Esteves, Bernardo Portela, João Paulo 0001   +3 more
openaire   +2 more sources

Secure Deduplication Method Based on Autoencoder

2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 2020
Message lock encryption is the basis of current secure deduplication research. However, since the theoretical basis of randomized Message-locked encryption (R-MLE) is bilinear mapping, the computational overhead is large. Although this method has semantic security and supports data deduplication, it is not practical.
Hui Qi, Tianwei Lu, Ligang Cong, Xiaoqiang Di   +3 more
openaire   +1 more source