Design and Prototype of a Unified Framework for Error-robust Compression and Encryption in IoT
- URL: http://arxiv.org/abs/2410.14396v1
- Date: Fri, 18 Oct 2024 12:00:06 GMT
- Title: Design and Prototype of a Unified Framework for Error-robust Compression and Encryption in IoT
- Authors: Gajraj Kuldeep, Qi Zhang,
- Abstract summary: Internet of Things (IoT) relies on resource-constrained devices for data acquisition.
Data compression and secrecy often lack energy efficiency for these devices.
We have developed the ENCRUST scheme, which combines compression, secrecy, and error recovery.
- Score: 8.081144369967793
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Internet of Things (IoT) relies on resource-constrained devices for data acquisition, but the vast amount of data generated and security concerns present challenges for efficient data handling and confidentiality. Conventional techniques for data compression and secrecy often lack energy efficiency for these devices. Compressive sensing has the potential to compress data and maintain secrecy, but many solutions do not address the issue of packet loss or errors caused by unreliable wireless channels. To address these issues, we have developed the ENCRUST scheme, which combines compression, secrecy, and error recovery. In this paper, we present a prototype of ENCRUST that uses energy-efficient operations, as well as a lighter variant called L-ENCRUST. We also perform security analysis and compare the performance of ENCRUST and L-ENCRUST with a state-of-the-art solution in terms of memory, encryption time, and energy consumption on a resource-constrained TelosB mote. Our results show that both ENCRUST and L-ENCRUST outperform the state-of-the-art solution in these metrics.
Related papers
- FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution [68.77813885751308]
State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information.<n>We propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data.<n>Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames.
arXiv Detail & Related papers (2025-06-13T07:59:52Z) - PoSyn: Secure Power Side-Channel Aware Synthesis [1.5649928667204427]
PoSyn is a novel logic synthesis framework designed to enhance cryptographic hardware resistance against PSC attacks.<n>We evaluate PoSyn across various cryptographic hardware implementations, including AES, RSA, PRESENT, and post-quantum cryptographic algorithms such as Saber and CRYSTALS-Kyber.
arXiv Detail & Related papers (2025-06-09T21:41:47Z) - Continuous-Variable Quantum Key Distribution with Composable Security and Tight Error Correction Bound for Constrained Devices [1.726266255043611]
Constrained devices, such as smart sensors, wearable devices, and Internet of Things nodes, rely on secure communications to function properly.
CV-QKD offers the highest secure key rate and the greatest versatility for integration into existing infrastructure.
arXiv Detail & Related papers (2025-04-08T19:08:08Z) - Security and Real-time FPGA integration for Learned Image Compression [8.824600702288848]
Learnable Image Compression (LIC) has proven capable of outperforming standardized video codecs in compression efficiency.
The present work addresses these challenges by providing an integrated workflow and platform for training, securing, and deploying LIC models on hardware.
We introduce a novel Quantization-Aware Watermarking (QAW) technique, where the model is watermarked during quantization using a joint loss function.
arXiv Detail & Related papers (2025-03-06T12:33:13Z) - TRUST: A Toolkit for TEE-Assisted Secure Outsourced Computation over Integers [30.72930396939045]
We propose a toolkit for TEE-assisted (Trusted Execution Environment) SOC over integers, named TRUST.
In terms of system architecture, TRUST falls in a single TEE-equipped cloud server only through seamlessly integrating the computation of REE (Rich Execution Environment) and TEE.
We present textttSEAT, secure data trading based on TRUST.
arXiv Detail & Related papers (2024-12-02T03:19:29Z) - Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
Anomaly and missing data constitute a thorny problem in industrial applications.
Deep learning enabled anomaly detection has emerged as a critical direction.
The data collected in edge devices contain user privacy.
arXiv Detail & Related papers (2024-11-06T15:38:31Z) - Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays [0.6435156676256051]
We present self-adaptive quantum kernel (SAQK) PCA as a superior alternative to enhance information retention.
Results highlight the potential of noisy intermediate-scale quantum computers to revolutionize data processing in real-world IoT applications.
arXiv Detail & Related papers (2024-08-28T04:07:40Z) - Efficient ECC-based authentication scheme for fog-based IoT environment [0.0]
A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed to improve the security of the private key and the time taken for key-pair generation.
Results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively.
arXiv Detail & Related papers (2024-08-05T20:47:49Z) - UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation [59.3877309501938]
Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios.
We introduce a codebook containing frequency domain information as a prior input to the INR network.
This enhances the representational power of INR and provides distinctive conditioning for different image blocks.
arXiv Detail & Related papers (2024-05-27T05:52:13Z) - Convolutional variational autoencoders for secure lossy image compression in remote sensing [47.75904906342974]
This study investigates image compression based on convolutional variational autoencoders (CVAE)
CVAEs have been demonstrated to outperform conventional compression methods such as JPEG2000 by a substantial margin on compression benchmark datasets.
arXiv Detail & Related papers (2024-04-03T15:17:29Z) - Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors [0.9831489366502302]
Homomorphic encryption schemes are promising solutions as they enable the processing and execution of operations on IoT data while still encrypted.
We propose a novel privacy-preserving anomaly detection solution designed for homomorphically encrypted data generated by IoT devices.
arXiv Detail & Related papers (2024-03-14T12:11:25Z) - NEQRX: Efficient Quantum Image Encryption with Reduced Circuit Complexity [2.7985570786346745]
We propose an efficient implementation scheme for a quantum image encryption algorithm combining the generalized affine transform and logistic map.
We achieve a remarkable 50% reduction in cost while maintaining security and efficiency.
arXiv Detail & Related papers (2022-04-14T10:15:23Z) - Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and
Deep Learning [49.3231734733112]
We show a modular and holistic approach that combines Deep Neural Networks (DNN) trained on simulated data, Product (TP) based Error-Correcting Codes (ECC) and a safety margin into a single coherent pipeline.
Our work improves upon the current leading solutions by up to x3200 increase in speed, 40% improvement in accuracy, and offers a code rate of 1.6 bits per base in a high noise regime.
arXiv Detail & Related papers (2021-08-31T18:21:20Z) - Analyzing and Mitigating JPEG Compression Defects in Deep Learning [69.04777875711646]
We present a unified study of the effects of JPEG compression on a range of common tasks and datasets.
We show that there is a significant penalty on common performance metrics for high compression.
arXiv Detail & Related papers (2020-11-17T20:32:57Z) - Attribution Preservation in Network Compression for Reliable Network
Interpretation [81.84564694303397]
Neural networks embedded in safety-sensitive applications rely on input attribution for hindsight analysis and network compression to reduce its size for edge-computing.
We show that these seemingly unrelated techniques conflict with each other as network compression deforms the produced attributions.
This phenomenon arises due to the fact that conventional network compression methods only preserve the predictions of the network while ignoring the quality of the attributions.
arXiv Detail & Related papers (2020-10-28T16:02:31Z) - Dynamic Compression Ratio Selection for Edge Inference Systems with Hard
Deadlines [9.585931043664363]
We propose a dynamic compression ratio selection scheme for edge inference system with hard deadlines.
Information augmentation that retransmits less compressed data of task with erroneous inference is proposed to enhance the accuracy performance.
Considering the wireless transmission errors, we further design a retransmission scheme to reduce performance degradation due to packet losses.
arXiv Detail & Related papers (2020-05-25T17:11:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.