A Security-Enhanced and Privacy-Preserving Certificateless Aggregate Signcryption Scheme-Based Artificial Neural Network in Wireless Medical Sensor Network
Runtao Ren, Jinqi Su
Abstract
With the recent advances in mobile sensing and beyond-5G technologies, wireless medical sensor networks (WMSNs) have become an emerging hot topic in smart healthcare. Under the condition of limited resources of medical sensor nodes (MSNs), ensuring the accuracy and integrity of a large number of patients’ medical data is a crux for security and data privacy issues. In order to overcome the limitations of medical sensors in communication and computing capabilities, and to achieve privacy in WMSN, we propose a low complexity certificateless aggregate signcryption (CL-ASC) scheme-based artificial neural network (NN) for safely achieving authentication and unforgeability of medical nodes. We use an adaptive sigma filter for denoising, adopt Levenshtein entropy for encoding, and design cryptographic protocol for signcrypting in the hidden layer of deep feedforward artificial NN (DFANN). The CL-ASC can authenticate all sensing bioinformation at once in a privacy-preserving way. Given the challenge of applying a discrete logarithm and computational Diffie-Hellman problems based on an elliptic curve, the proposed scheme satisfies the indistinguishability against chosen-ciphertext attacks (IND-CCA) and the adaptive chosen message attack (UF-CMA) security properties under the random oracle model (ROM). Finally, the benchmark evaluation shows that ours is ahead of similar state-of-the-art schemes, due to its low complexity and higher computational efficiency. Researchers interested in the provable security protocol design and framework of NNs can gain useful insights from this research and identify future cross domain of cybersecurity and NNs research directions based on the proposed scheme herein.