Design Prototype and Security Analysis of a Lightweight Joint Compression and Encryption Scheme for Resource-constrained IoT Devices

TY - JOUR

T1 - Design Prototype and Security Analysis of a Lightweight Joint Compression and Encryption Scheme for Resource-constrained IoT Devices

AU - Kuldeep, Gajraj

AU - Zhang, Qi

PY - 2022/1/1

Y1 - 2022/1/1

N2 - Compressive sensing (CS) can provide joint compression and encryption, which is promising to address the challenges of massive sensor data and data security in the Internet of Things (IoT). However, as IoT devices have constrained memory, computing power, and energy, in practice the CS-based computationally secure scheme is shown to be vulnerable to ciphertext-only attack for short-signal length. Although the CS-based perfectly secure scheme has no such vulnerabilities, its practical realization is challenging. In this article, we propose an energy concealment (EC) encryption scheme, a practical realization of the perfectly secure scheme by concealing energy, thereby removing the requirement of an additional secure channel. We propose three different methods to generate sensing matrix to improve energy efficiency using linear feedback shift registers and lagged Fibonacci sequences. Leveraging the signal's maximum energy in the EC scheme, we design a new measure to evaluate reconstructed signal quality without the knowledge of the original signal. Furthermore, a new CS decoding algorithm is designed by incorporating the knowledge of maximum energy at the decoder, which improves the signal reconstruction quality while reducing the number of measurements. Additionally, our comprehensive security analysis shows that the EC scheme is secure against various cryptographic attacks. We implement the EC scheme using the three different ways of generating the sensing matrix in the resource-constrained TelosB mote using the Contiki operating system. The experimental results demonstrate that the EC scheme outperforms advanced encryption standard in terms of code memory footprint and total energy consumption.

AB - Compressive sensing (CS) can provide joint compression and encryption, which is promising to address the challenges of massive sensor data and data security in the Internet of Things (IoT). However, as IoT devices have constrained memory, computing power, and energy, in practice the CS-based computationally secure scheme is shown to be vulnerable to ciphertext-only attack for short-signal length. Although the CS-based perfectly secure scheme has no such vulnerabilities, its practical realization is challenging. In this article, we propose an energy concealment (EC) encryption scheme, a practical realization of the perfectly secure scheme by concealing energy, thereby removing the requirement of an additional secure channel. We propose three different methods to generate sensing matrix to improve energy efficiency using linear feedback shift registers and lagged Fibonacci sequences. Leveraging the signal's maximum energy in the EC scheme, we design a new measure to evaluate reconstructed signal quality without the knowledge of the original signal. Furthermore, a new CS decoding algorithm is designed by incorporating the knowledge of maximum energy at the decoder, which improves the signal reconstruction quality while reducing the number of measurements. Additionally, our comprehensive security analysis shows that the EC scheme is secure against various cryptographic attacks. We implement the EC scheme using the three different ways of generating the sensing matrix in the resource-constrained TelosB mote using the Contiki operating system. The experimental results demonstrate that the EC scheme outperforms advanced encryption standard in terms of code memory footprint and total energy consumption.

KW - Encoding

KW - Encryption

KW - Energy measurement

KW - Internet of Things

KW - Internet of Things.

KW - Length measurement

KW - Sensors

KW - Sparse matrices

KW - compressive sensing

KW - energy efficiency

KW - information security

KW - joint compression and encryption

KW - sensing matrix generation

KW - signal reconstruction

UR - http://www.scopus.com/inward/record.url?scp=85111048133&partnerID=8YFLogxK

U2 - 10.1109/JIOT.2021.3098859

DO - 10.1109/JIOT.2021.3098859

M3 - Journal article

SN - 2327-4662

VL - 9

SP - 165

EP - 181

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

IS - 1

ER -