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AuthorBaccour, Emna
AuthorErbad, Aiman
AuthorMohamed, Amr
AuthorHamdi, Mounir
AuthorGuizani, Mohsen
Available date2022-10-19T14:11:35Z
Publication Date2022-01-01
Publication NameIEEE Transactions on Network Science and Engineering
Identifierhttp://dx.doi.org/10.1109/TNSE.2022.3165472
CitationBaccour, E., Erbad, A., Mohamed, A., Hamdi, M., & Guizani, M. (2022). RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems. IEEE Transactions on Network Science and Engineering.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85127733713&origin=inward
URIhttp://hdl.handle.net/10576/35233
AbstractAlthough Deep Neural Networks (DNN) have become the backbone technology of several ubiquitous applications, their deployment in resource-constrained machines, e.g., Internet of Things (IoT) devices, is still challenging. To satisfy the resource requirements of such a paradigm, collaborative deep inference with IoT synergy was introduced. However, the distribution of DNN networks suffers from severe data leakage. Various threats have been presented, including black-box attacks, where malicious participants can recover arbitrary inputs fed into their devices. Although many countermeasures were designed to achieve privacy-preserving DNN, most of them result in additional computation and lower accuracy. In this paper, we present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy, without sacrificing the model performance. Particularly, we examine different DNN partitions that make the model susceptible to black-box threats and we derive the amount of data that should be allocated per device to hide proprieties of the original input. We formulate this methodology, as an optimization, where we establish a trade-off between the latency of co-inference and the privacy-level of data. Next, to relax the optimal solution, we shape our approach as a Reinforcement Learning (RL) design that supports heterogeneous devices as well as multiple DNNs/datasets.
Languageen
PublisherIEEE Computer Society
Subjectblack-box
distributed DNN
IoT devices
reinforcement learning
resource constraints
sensitive data
TitleRL-DistPrivacy: Privacy-Aware Distributed Deep Inference for Low Latency IoT Systems
TypeArticle
Issue Number4
Volume Number9
dc.accessType Abstract Only


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