Research on privacy and secure storage protection of personalized medical data based on hybrid encryption

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

EURASIP Journal on Information Security Pub Date : 2024-01-02 DOI:10.1186/s13635-023-00150-3

Jialu Lv

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引用次数: 0

Abstract

Personalized medical data privacy and secure storage protection face serious challenges, especially in terms of data security and storage efficiency. Traditional encryption and storage solutions cannot meet the needs of modern medical data protection, which has led to an urgent need for new data protection strategies. Research personalized medical data privacy and secure storage protection based on hybrid encryption, in order to improve the security and efficiency of data storage. A hybrid encryption mechanism was proposed, which uses user attributes as keys for data encryption. The results show that the storage consumption of user attribute keys increases with the number of user attributes, but the consumption of hybrid encryption privacy storage technology is much smaller than that of traditional schemes. In the test, when the number of users increased to 30, the processing time first reached 1200 ms. During the increase in data volume, both test data and real data showed a brief decrease in attack frequency, but after the data volume reached 730–780, the attack frequency increased. It is worth noting that the performance of test data is better than that of real data. Personalized medical data privacy and secure storage protection based on hybrid encryption can not only effectively improve data security and reduce the risk of attack, but also greatly outperform traditional solutions in storage consumption and processing time. It has important practical significance for modern medical data storage protection.

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基于混合加密的个性化医疗数据隐私和安全存储保护研究

个性化医疗数据隐私和安全存储保护面临严峻挑战，尤其是在数据安全和存储效率方面。传统的加密和存储解决方案无法满足现代医疗数据保护的需求，因此迫切需要新的数据保护策略。研究基于混合加密的个性化医疗数据隐私和安全存储保护，以提高数据存储的安全性和效率。提出了一种混合加密机制，将用户属性作为数据加密的密钥。结果表明，用户属性密钥的存储消耗随用户属性数量的增加而增加，但混合加密隐私存储技术的消耗远小于传统方案。在测试中，当用户数量增加到 30 个时，处理时间首先达到了 1200 毫秒。在数据量增加的过程中，测试数据和真实数据的攻击频率都出现了短暂的下降，但在数据量达到 730-780 后，攻击频率有所上升。值得注意的是，测试数据的性能优于真实数据。基于混合加密的个性化医疗数据隐私安全存储保护不仅能有效提高数据安全性，降低攻击风险，而且在存储消耗和处理时间上大大优于传统解决方案。这对现代医疗数据存储保护具有重要的现实意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EURASIP Journal on Information Security COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

8.80

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： The overall goal of the EURASIP Journal on Information Security, sponsored by the European Association for Signal Processing (EURASIP), is to bring together researchers and practitioners dealing with the general field of information security, with a particular emphasis on the use of signal processing tools in adversarial environments. As such, it addresses all works whereby security is achieved through a combination of techniques from cryptography, computer security, machine learning and multimedia signal processing. Application domains lie, for example, in secure storage, retrieval and tracking of multimedia data, secure outsourcing of computations, forgery detection of multimedia data, or secure use of biometrics. The journal also welcomes survey papers that give the reader a gentle introduction to one of the topics covered as well as papers that report large-scale experimental evaluations of existing techniques. Pure cryptographic papers are outside the scope of the journal. Topics relevant to the journal include, but are not limited to: • Multimedia security primitives (such digital watermarking, perceptual hashing, multimedia authentictaion) • Steganography and Steganalysis • Fingerprinting and traitor tracing • Joint signal processing and encryption, signal processing in the encrypted domain, applied cryptography • Biometrics (fusion, multimodal biometrics, protocols, security issues) • Digital forensics • Multimedia signal processing approaches tailored towards adversarial environments • Machine learning in adversarial environments • Digital Rights Management • Network security (such as physical layer security, intrusion detection) • Hardware security, Physical Unclonable Functions • Privacy-Enhancing Technologies for multimedia data • Private data analysis, security in outsourced computations, cloud privacy