Abstract
As people become more conscious of private information security and advancements in Electrocardiogram (ECG) identity identification technology, the need to encrypt private data intensifies. New wearable medical devices continuously gather vast amounts of personal health data, underscoring the urgency of safeguarding privacy. This paper focuses on encrypting and decrypting rich characteristic signals like ECG. It delves into the core aspects, including the logic circuit diagram of a four-bit digit absolute comparator using Complementary Metal Oxide Semiconductor (CMOS) technology and optimizing delay through logic effort to reduce power consumption. XOR gates and other gate circuits, as well as complement subtraction principles, are employed in building logic gates. Calculating delay involves selecting the longest critical path and determining the specific parameters (g, h, and p) for each logic gate. Subsequently, energy consumption is computed using a formula, revealing a trade-off between energy and delay. After optimization, the circuit's energy consumption is 184.832, and the delay is 34.13. In summary, the 4-bit absolute value detector plays a crucial role in encryption, addressing the growing concern for privacy and data security.
Publisher
Darcy & Roy Press Co. Ltd.
Reference10 articles.
1. Arin G, Jianwei Z, Hesham E, et al. Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021.
2. Zeng Jixin. Development of identity recognition system based on finger ECG signal. Hangzhou Dianzi University, 2015.
3. Chiu C C, Chuang C M, Hsu C Y. A novel personal identity verification approach using a discrete wavelet transform of the ECG signal. 2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008). IEEE, 2008: 201-206.
4. Aranda A, Karel J, Bonizzi P, et al. Acute MI Detection Derived From ECG Parameters Distribution. IEEE, 2019.
5. Li Zhiqiang, Ren Xiaohong, Lin Yongwu. Research on key techniques of image-based ECG waveform detection. Chinese Journal of Metrology, 2023, (02):110-113+121.