Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry
- Title
- Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry
- Creator
- Kumar P.; Laroia A.; Kumar M.; Laroia A.; Upreti K.; Parashar J.
- Description
- Securing electronic health records (EHRs) in the Internet of Medical Things (IoMT) ecosystem is a key concern in healthcare due to the sector's differed environment. As the evolution of technology continues, ensuring the confidentiality, integrity, and accessibility of EHRs becomes more and more challenging. To enhance the confidentiality of healthcare picture data, this study explores the combined use of deep learning and cryptography methods. Through the utilization of weight analysis for improving encryption strength and the combination of chaotic systems to generate undetectable encryption patterns, it explores how deep neural networks can be modified for use in encryption. It also provides a survey of the present scenario of deep learning-based image detection of anomalies methods in working environments, such as network typologies, supervision levels, and assessment norms. Techniques in cryptography provide an effective means to protect confidential medical picture data while it's being transmitted and stored. Deep learning, on the other hand, has the ability to entirely change cryptography by providing robust encryption, resolution augmentation, and detection capabilities for medical image security. The paper outlines future research approaches to overcome these problems and tackles the opportunities and obstacles in medical image cryptography and industrial picture anomaly detection. Through this work, picture privacy in the healthcare and industrial sectors is advanced, opening the door to enhanced privacy, integrity, and availability of vital image data by overcoming the gap between deep learning and cryptography. 2024 IEEE.
- Source
- 2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 436-441.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Cryptography; Deep Learning; Image Encryption; Medical Image Security
- Coverage
- Kumar P., Maharshi Dayanand University Computer Science Engineering, Haryana, India; Laroia A., University of Texas at Dallas Ms in Business Analytics, TX, United States; Kumar M., Amity University Computer Science Engineering, Uttar Pradesh, India; Laroia A., Birla Institute of Technology, Civil Engineering, Pilani, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR Campus, India; Parashar J., Dept. of Computer Science and Engineering, Delhi, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035326-6
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kumar P.; Laroia A.; Kumar M.; Laroia A.; Upreti K.; Parashar J., “Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/18986.