RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for Low Latency IoT Systems

Authors

Emna Baccour, Hamad Bin Khalifa University, College of Science and EngineeringFollow
Aiman Erbad, Hamad Bin Khalifa University, College of Science and Engineering
Amr Mohamed, Qatar University
Mounir Hamdi, Hamad Bin Khalifa University, College of Science and Engineering
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Network Science and Engineering

Abstract

Although 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.

First Page

2066

Last Page

2083

DOI

10.1109/TNSE.2022.3165472

Publication Date

4-6-2022

Keywords

black-box, distributed DNN, IoT devices, reinforcement learning, resource constraints, sensitive data

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Recommended Citation

E. Baccour, A. Erbad, A. Mohamed, M. Hamdi and M. Guizani, "RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for Low Latency IoT Systems," in IEEE Transactions on Network Science and Engineering, vol. 9, no. 4, pp. 2066-2083, 1 July-Aug. 2022, doi: 10.1109/TNSE.2022.3165472.