Proposed method of protection of fixed length samples through using Paillier cryptosystem
146 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.81.2022.148-155Keywords:
Privacy; Cryptosystem; Homomorphic; Euclidean distances; Paillier cryptosystem; Fingerprint.Abstract
Privacy of databases is currently a very concern, there are many scandals of using customer information for personal gain caused by many different technology companies that own large databases of information about customers. The problem is how the user can provide a database that meets the algorithm used in the authentication model, giving accurate results without revealing private information or personal data. Theoretical research on homomorphic cryptosystems and public encryption can solve problems of user privacy security. The paper presents the basic issues of the homomorphic algorithm and the Paillier cryptosystem; computational similarity using the Euclidean distances for samples of fixed length. Sample experimental results coded by Paillier cryptosystem then check the correct calculation by calculating, comparing the length with a defined threshold to remove the inappropriate samples and decide the combination of the system. From there, propose a method of protection of fixed-length samples.
References
[1]. Jadhav, Barbadekar, Patil, “Euclidean Distance Based Fingerprint Matching,” Recent Researches in Communications, Automation, Signal Processing, Nanotechnology, Astronomy and Nuclear Physics, ISBN: 978-960-474-276-9.
[2]. Jonathan Katz, Yehuda Lindell, “Introduction to Modern Cryptography: Principles and Protocols,” Chapman & Hall/CRC, (2007).
[3]. Khuất Thanh Sơn. “Mã hóa đồng cấu đầy đủ và ứng dụng trong theo dõi sức khỏe an toàn dựa trên điện toán đám mây”. Luận văn Thạc sĩ, Đại học công nghệ - Đại học quốc gia Hà Nội, (2021).
[4]. Marta G´omez Barrero, “Improving Security and Privacy in Biometric systems,” Ingeniero de Inform´atica y Licenciada en Matem´aticas Universidad Aut´onoma de Madrid, SPAIN, (2016).
[5]. Mark A. Will, Ryan K.L. Ko, “A guide to homomorphic encryption,” in The Cloud Security Ecosystem, (2015).
[6]. Neeraj Bhargava, Anchal Kumawat, Ritu Bhargava, “Fingerprint Matching of Normalized Image based on Euclidean Distance,” International Journal of Computer Applications (0975 – 8887)Volume 120 – No.24, (2015). DOI: https://doi.org/10.5120/21409-4428
[7]. Hsin-Tsung Peng, William W.Y. Hsu, “Homomorphic Encryption Application on FinancialCloud Framework”, The Ministry of Science and Technology of Taiwan.
[8]. Leon J. Helsloot; Gamze Tillem, Zekeriya Erkin, “Privacy-preserving online behavioral advertising using homomorphic encryption,” IEEE Workshop on Information Forensics and Security, (2017). DOI: https://doi.org/10.1109/WIFS.2017.8267662
[9]. Marta G´omez Barrero, “Improving Security and Privacy in Biometric systems,” Ingeniero de Inform´atica y Licenciada en Matem´aticas Universidad Aut´onoma de Madrid, Spain, (2016).
