scholarly journals R-OO-KASE: Revocable Online/Offline Key Aggregate Searchable Encryption

2020 ◽  
Vol 5 (4) ◽  
pp. 391-418
Author(s):  
Mukti Padhya ◽  
Devesh C. Jinwala
Keyword(s):  
Radio Frequency Identification ◽  
Keyword Search ◽  
Electrical Power ◽  
Computational Cost ◽  
Security Analysis ◽  
Searchable Encryption ◽  
Resource Constrained ◽  
Encryption Decryption ◽  
Resource Constrained Devices ◽  
Constrained Devices

Abstract The existing Key Aggregate Searchable Encryption (KASE) schemes allow searches on the encrypted dataset using a single query trapdoor, with a feature to delegate the search rights of multiple files using a constant size key. However, the operations required to generate the ciphertext and decrypt it in these schemes incur higher computational costs, due to the computationally expensive pairing operations in encryption/decryption. This makes the use of such schemes in resource-constrained devices, such as Radio Frequency Identification Devices, Wireless Sensor Network nodes, Internet of Things nodes, infeasible. Motivated with the goal to reduce the computational cost, in this paper, we propose a Revocable Online/Offline KASE (R-OO-KASE) scheme, based on the idea of splitting the encryption/decryption operations into two distinct phases: online and offline. The offline phase computes the majority of costly operations when the device is on an electrical power source. The online phase generates final output with the minimal computational cost when the message (or ciphertext) and keywords become known. In addition, the proposed scheme R-OO-KASE also offers multi-keyword search capability and allows the data owners to revoke the delegated rights at any point in time, the two features are not supported in the existing schemes. The security analysis and empirical evaluations show that the proposed scheme is efficient to use in resource-constrained devices and provably secure as compared to the existing KASE schemes.

SIBSC: Separable Identity-Based Signcryption for Resource-Constrained Devices

Informatica ◽  
2017 ◽  
Vol 28 (1) ◽  
pp. 193-214 ◽  
Author(s):  
Tung-Tso Tsai ◽  
Sen-Shan Huang ◽  
Yuh-Min Tseng
Keyword(s):  
Resource Constrained ◽  
Identity Based ◽  
Resource Constrained Devices ◽  
Constrained Devices

scholarly journals Emotion Recognition on Edge Devices: Training and Deployment

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4496
Author(s):  
Vlad Pandelea ◽  
Edoardo Ragusa ◽  
Tommaso Apicella ◽  
Paolo Gastaldo ◽  
Erik Cambria
Keyword(s):  
Emotion Recognition ◽  
Language Processing ◽  
Computational Cost ◽  
Sequential Learning ◽  
High Quality ◽  
Fast Training ◽  
Online Sequential Learning ◽  
And Performance ◽  
Resource Constrained Devices ◽  
Constrained Devices

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.

RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study

2021 ◽  
Vol 5 (4) ◽  
pp. 1-28
Author(s):  
Chia-Heng Tu ◽  
Qihui Sun ◽  
Hsiao-Hsuan Chang
Keyword(s):  
Neural Networks ◽  
Environmental Monitoring ◽  
Convolutional Neural Networks ◽  
Model Building ◽  
Software Framework ◽  
Training Procedure ◽  
Monitoring Methods ◽  
Resource Constrained ◽  
Resource Constrained Devices ◽  
Constrained Devices

Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.

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