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Automatic Control and Computer Sciences

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01-12-2023

Approach for Protecting Mobile Device User’s Data Based on Multifactor Authentication, Visual Cryptography, and Steganography

Author: N. N. Shenets

Published in: Automatic Control and Computer Sciences | Issue 8/2023

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Abstract

An approach is proposed to protect user’s personal data stored on mobile devices running the Android OS, which combines multifactor authentication, secret sharing, visual cryptography, and steganography; and it also uses the built-in security features of the operating system. It ensures the confidentiality of personal data even if both the mobile device and the related remote storage (server) are compromised.

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Shenets, N., Multi-factor one-way authentication and key retrieval based on ideal threshold secret sharing, Nonlinear Phenom. Complex Syst., 2020, vol. 23, no. 1, pp. 54–65. https://doi.org/10.33581/1561-4085-2020-23-1-54-65CrossRef

Asmuth, C. and Bloom, J., A modular approach to key safeguarding, IEEE Trans. Inf. Theory, 1983, vol. 29, no. 2, pp. 208–210. https://doi.org/10.1109/tit.1983.1056651MathSciNetCrossRef

Blakley, G.R., Safeguarding cryptographic keys, 1979 Int. Workshop on Managing Requirements Knowledge (MARK), New York, 1979, IEEE, 1979, pp. 313–318. https://doi.org/10.1109/mark.1979.8817296

Chen, L., Laing, T.M., and Martin, K.M., Efficient, XOR-based, Ideal (t,n)-threshold schemes, Cryptology and Network Security, Foresti, S. and Persiano, G., Eds., Lecture Notes in Computer Science, vol. 10052, Cham: Springer, 2016, pp. 467–483. https://doi.org/10.1007/978-3-319-48965-0_28CrossRef

Karnin, E., Greene, J., and Hellman, M., On secret sharing systems, IEEE Trans. Inf. Theory, 1983, vol. 29, no. 1, pp. 35–41. https://doi.org/10.1109/tit.1983.1056621MathSciNetCrossRef

Shamir, A., How to share a secret, Commun. ACM, 1979, vol. 22, no. 11, pp. 612–613. https://doi.org/10.1145/359168.359176MathSciNetCrossRef

Shenets, N.N., On information level of modular circuits of sharing a secret, Dokl. Nats. Akad. Nauk Belarusi, 2010, vol. 54, no. 6, pp. 9–12.MathSciNet

Pedersen, T.P., A threshold cryptosystem without a trusted party, Advances in Cryptology—EUROCRYPT ’91, Davies, D.W., Ed., Lecture Notes in Computer Science, vol. 547, Berlin: Springer, 1991, pp. 522–526. https://doi.org/10.1007/3-540-46416-6_47CrossRef

Naor, M. and Shamir, A., Visual cryptography, Advances in Cryptology—EUROCRYPT’94, De Santis, A., Ed., Lecture Notes in Computer Science, vol. 950, Berlin: Springer, 1995, pp. 1–12. https://doi.org/10.1007/bfb0053419

10.

Nimmy, K. and Sethumadhavan, M., Novel mutual authentication protocol for cloud computing using secret sharing and steganography, The Fifth Int. Conf. on the Applications of Digital Information and Web Technologies (ICADIWT 2014), Chennai, India, 2014, IEEE, 2014, pp. 101–106. https://doi.org/10.1109/icadiwt.2014.6814685

11.

Mavlonov, O.N., Yusupov, R.Z., and Turdibekov, B.B., Steganography: Methods for hiding data, Int. J. Contemp. Sci. Tech. Res., 2022, vol. 1, no. 2, pp. 56–58. doi https://zenodo.org/doi/10.5281/zenodo.7304213

12.

Kozachok, A., Spirin, A., Kozachok, V., and Lavrova, D., Information leakage prevention based on the separation of encrypted and compressed data, 2021 Ivannikov Ispras Open Conf. (ISPRAS), Moscow, 2021, IEEE, 2021, pp. 88–97. https://doi.org/10.1109/ispras53967.2021.00009

13.

Nosrati, M., Karimi, R., and Hariri, M., An introduction to steganography methods, World Appl. Program., 2011, vol. 1, no. 3, pp. 191–195.

14.

Kadhum, R.N. and Ali, N.H.M., Data hiding and its applications: Digital watermarking and steganography, Int. J. Nonlinear Anal., 2022, vol. 13, no. 1, pp. 4001–4011. https://doi.org/10.3390/books978-3-0365-2937-0CrossRef

15.

Aleksandrova, E.B., Shtyrkina, A.A., and Yarmak, A.V., Post-quantum group-oriented authentication in IoT, Nonlinear Phenom. Complex Syst., 2020, vol. 23, no. 4, pp. 405–413. https://doi.org/10.33581/1561-4085-2020-23-4-405-413CrossRef

16.

Altuwaijri, H. and Ghouzali, S., Android data storage security: A review, J. King Saud Univ. Comput. Inf. Sci., 2020, vol. 32, no. 5, pp. 543–552. https://doi.org/10.1016/j.jksuci.2018.07.004CrossRef

17.

Zegzhda, P., Zegzhda, D., Pavlenko, E., and Ignatev, G., Applying deep learning techniques for Android malware detection, Proc. 11th Int. Conf. on Security of Information and Networks, Cardiff, Wales, 2018, New York: Association for Computing Machinery, 2018, p. 7. https://doi.org/10.1145/3264437.3264476

18.

Trusty TEE | Android Open Source Project. https://source.android.com/docs/security/features/trusty. Cited January 10, 2023.

19.

KeyStore | Android Developers. https://developer.android.com/reference/java/security/KeyStore. Cited January 10, 2023.

20.

Biometrics | Android Open Source Project. https://source.android.com/docs/security/features/biometric. Cited January 10, 2023.

21.

Usov, E.S., Nikol’skii, A.V., Pavlenko, E.Yu., and Zegzhda, D.P., Architecture of the protected cloud data storage using Intel SGX technology, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 1144–1149. https://doi.org/10.3103/s0146411618080394CrossRef

Title: Approach for Protecting Mobile Device User’s Data Based on Multifactor Authentication, Visual Cryptography, and Steganography
Author: N. N. Shenets
Publication date: 01-12-2023
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 8/2023
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411623080291

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