Visible to the public Mathematical cryptanalysis of \#x201C;personalized information encryption using ECG signals with chaotic functions \#x201D;

TitleMathematical cryptanalysis of \#x201C;personalized information encryption using ECG signals with chaotic functions \#x201D;
Publication TypeConference Paper
Year of Publication2017
AuthorsGençoğlu, M. T.
Conference Name2017 International Conference on Computer Science and Engineering (UBMK)
Date Publishedoct
Keywordsbifurcation, chaos, chaotic cryptography, chaotic functions, chaotic system, Chosen-plaintext attack, composability, cryptanalysis, cryptography, cryptosystem, data encryption algorithm, ECG signals, electrocardiogram, Electrocardiography, Henon map, Henon mapping, Heuristic algorithms, image data encryption, mathematical cryptanalysis, Metrics, numerical simulation, Optics, personalized information encryption, Physics, pubcrawl, Resiliency, Secret key, text analysis, text encryption
Abstract

The chaotic system and cryptography have some common features. Due to the close relationship between chaotic system and cryptosystem, researchers try to combine the chaotic system with cryptosystem. In this study, security analysis of an encryption algorithm which aims to encrypt the data with ECG signals and chaotic functions was performed using the Logistic map in text encryption and Henon map in image encryption. In the proposed algorithm, text and image data can be encrypted at the same time. In addition, ECG signals are used to determine the initial conditions and control parameters of the chaotic functions used in the algorithm to personalize of the encryption algorithm. In this cryptanalysis study, the inadequacy of the mentioned process and the weaknesses of the proposed method have been determined. Encryption algorithm has not sufficient capacity to provide necessary security level of key space and secret key can be obtained with only one plaintext/ciphertext pair with chosen-plaintext attack.

DOI10.1109/UBMK.2017.8093445
Citation Keygencoglu_mathematical_2017