DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT ...
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In this paper, we introduce a methodology aiming to secure the sensitive data through re-thinking the distribution strategy, without adding any computation ...
With such a server-centric approach, the large data volume (e.g., videos) and growing transmission latency have become problematic, particularly for decisions.
The authors in [9] explored distributing DNN over IoT devices in a surveillance system to minimize the latency of making the classification decision and improve ...
In this paper, we present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy, without sacrificing ...
In this paper, we introduce a methodology aiming to secure the sensitive data through re-thinking the distribution strategy, without adding any computation ...
RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for Low Latency IoT Systems. July 2022; IEEE Transactions on Network Science and Engineering 9(4):1-1.
DistPrivacy: Privacy-aware distributed deep neural networks in IoT surveillance systems. E Baccour, A Erbad, A Mohamed, M Hamdi, M Guizani. GLOBECOM 2020-2020 ...
Aug 27, 2022 · Guizani,. ”DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in. IoT surveillance systems,” 2020 arXiv:2010.13234. Emna Baccour ...
In this paper, we present an approach that targets the privacy of collaborative inference via controlling the amount of data assigned to different participants.
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DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT surveillance systems · Computer Science, Engineering. GLOBECOM 2020 - 2020 IEEE Global ...