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RETRACTED ARTICLE: Research on privacy and secure storage protection of personalized medical data based on hybrid encryption

This article was retracted on 10 May 2024

This article has been updated


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.

1 Introduction

In the information age, personalized medical services have become a hot topic in the current medical field. In order to improve the quality of medical services, medical institutions rely on a large amount of personalized patient data, including medical history, diagnostic results, vital sign monitoring data, etc. However, these data are very sensitive and confidential. If not fully protected, the leakage of medical data not only violates the privacy of patients but also may lead to legal risks for medical institutions, seriously affecting their service quality and reputation. Therefore, how to effectively protect these sensitive data and ensure its security and integrity has become a very important issue [1, 2]. Due to the lack of sufficient flexibility and efficiency, traditional encryption methods are no longer able to meet the high requirements for data protection in modern medical services. For this reason, hybrid encryption technology has emerged, which combines the efficiency of symmetric encryption with the security of asymmetric encryption. Specifically, in the transmission process, hybrid encryption technology first uses asymmetric encryption methods to encrypt the key of the data and then uses symmetric encryption methods to encrypt the data itself. This not only ensures the security of medical data but also meets the needs of real-time and efficient medical services [3, 4]. The main purpose of the research is to design and implement a secure data storage and processing architecture based on hybrid encryption while taking into account data availability, confidentiality, and integrity. On this basis, the study will also explore how to apply this technology to specific medical scenarios to address privacy and security issues. We will also explore how to apply this technology to address privacy and security issues in specific medical scenarios [5, 6]. I hope that through this study, we can not only improve the storage and processing security of medical data but also provide a practical data protection solution for medical service providers, ultimately paving the way for information-based medical services and providing a reliable bridge. The research will be carried out in four parts. The first part is an overview of the design of personalized medical data privacy secure storage protection based on hybrid encryption. The second part is a study of personalized medical data privacy secure storage protection models based on hybrid encryption. The third part is an experimental verification of the second part. The fourth part is a summary of the research content and points out the shortcomings.

2 Related works

With the development of personalized healthcare, the privacy and secure storage protection of medical data has become an important research field, among which the privacy and secure storage protection of personalized medical data based on hybrid encryption is particularly crucial. Yang Y et al. proposed a privacy-preserving medical system using non-deterministic finite automata (NFA) and demonstrated that P-Med implemented treatment program recommendations without disclosing privacy to unauthorized parties [7]. Hafsa A et al. proposed an image encryption model based on a composite chaotic pseudorandom number generator and an improved advanced encryption standard. The experimental results demonstrate that the proposed mapping has a sufficiently large key space, and compared to the AES standard, the proposed image encryption algorithm increases the entropy of the encrypted image and reduces complexity time by 97% [8]. Prasad V et al. proposed some mechanical and auxiliary tumor processes for allocating health resources, and proposed new methods for using these resources in the era of artificial intelligence, in order to make human life a part of this process and explore the favorable conditions shared by the medical and computer industries [9]. Wei et al. used the Dual Pair Vector Space (DPVS) technique to propose an encryption scheme with a fixed length of exposed parameters. The scheme combined ciphertext ciphertext-dependent access control vector with the random matrix. This method had good practicability [10]. Lee et al. suggested a new image encryption scheme that used hybrid techniques to encrypt one or more images of different types of images simultaneously. The hybrid model followed a nonlinear function based on Cramer’s rules. Due to the use of one-dimensional mapping, the designed cryptosystem was fast and had a large key space, which could resist all usual attacks [11].

This type of research mainly explores the application of hybrid encryption technology in medical data protection, especially the impact on secure storage efficiency and data attack risk. Yuan H et al. used message lock encryption to implement deduplication of ciphertext. The user encrypts sensitive data with an aggregated key, and the CSP compares the stored data with the newly uploaded data. CSP did not store duplicate data to save storage space [12]. Liu et al. proposed a new searchable encryption scheme. The solution supports attribute-based keyword search and deduplication, and each shared file can generate a data label to complete the deduplication. In this study, the third-party audit and hash function are combined to ensure data integrity, and the outsourcing decryption method is used to optimize the problem of heavy encryption calculation [13]. Rafique A et al. proposed CryptoDICE, a distributed data protection system aimed at addressing the challenges of data security and privacy in cloud storage. CryptoDICE integrates multiple data encryption schemes and supports user-defined functionality (UDF) for heterogeneous NoSQL databases. The experimental evaluation work shows that the performance overhead of CryptoDICE is acceptable and can achieve low latency aggregation queries, successfully verifying its practicality in industrial SaaS application environments [14]. Fedotov S et al. proposed using femtosecond laser pulses to write two types of polarization-sensitive birefringent structures. The study revealed the dependence of delay and birefringence slow axis on laser exposure parameters, demonstrating the possibility of highly secure data storage based on different thermal behaviors of laser-modified regions [15].

To sum up, currently, more medical institutions will choose to use electronic information systems to store medical data. However, data is subject to a variety of security threats involving data privacy, integrity, and authentication of data. The above researchers have proposed various encryption algorithms and schemes to solve different problems. Aiming at the problems of medical data privacy security storage and protection, this study proposed a privacy security storage and protection design of private medical information with mixed encryption to solve the verification problem of various indicators. In the study, a total of 12 researchers reviewed the paper. They mainly judge whether the paper represents the latest technological level based on several criteria such as its innovation, practicality, and driving effect on the current technological level. These reviewers are mainly distributed in China, Australia, the USA, and Germany. The keywords of the article include mixed encryption, medical data, private data storage, key decoding, and data plaintext, among which mixed encryption and medical data are relatively popular. According to the author’s keywords, the main topics of this paper are the application of hybrid encryption technology, security protection of medical data, and optimization of private data storage.

3 Personalized medical data privacy security storage protection design based on mixed encryption

In this study, a healthcare data storage scheme based on hybrid encryption technology is adopted. First, the public key and private key are combined to achieve the pre-encryption of sensitive individual health data, so that it is converted from “true” information to “ciphertext” to enhance its security performance. The confidential medical information is stored in the cloud for users to access at any time to ensure personal privacy. The scheme not only ensures the safety of the system, but also ensures the operating efficiency of the system, and effectively reduces the data leakage in the system.

3.1 Hybrid encryption model construction

Medical data for medical center collections can be stored and accessed. After the Adversarial Biasing and Fairness (ABF) filter is introduced, the user attribute information in the access policy can completely hide the problem of the access policy. In smart medicine, users can be called “authorized” users when their data access attributes conform to the access policy set by the patient when uploading. When the user is authorized, the intermediate value that is not related to the ciphertext of the user’s personal health data is first generated in the outsourced cloud and then returned to the user to improve the efficiency of decrypting the user’s personal health data. For the storage of private data, the construction scheme of a hybrid encryption model is designed, as shown in Fig. 1.

Fig. 1
figure 1

Mixed encryption model diagram

In Fig. 1, the Data Owner (DO) first loads the medical data into the hospital's local server to ensure data security. A DO is usually a doctor or researcher within a medical institution. A Data User (DU) is a doctor or researcher in another medical institution. If DU satisfies the access policy, DU can decrypt the ciphertext of medical data. Otherwise, DU is an unauthorized user. In a hospital Local network, the hospital Local Server (LS) is a trusted entity. The main work of LS has two aspects: the encryption of medical data and the management of key. The Semi-trust Attribute Authority (STA) is a semi-trusted third party authorized by healthcare organizations. Its main task is to generate a partial public parameter to encrypt the symmetric key and a user attribute key to decrypt it. Cloud Storage Server (CSS) serves only as a platform for storing and sharing encrypted medical data. The Outsourced Cloud Server (OCS) is a semi-trusted entity in the medical information system [16, 17]. OCS not only generates some public ciphertext and user feature ciphertext but also decrypts some ciphertext by authorized users. The hybrid encryption model combines the advantages of symmetric and asymmetric encryption, aiming to achieve the security and efficiency of medical data storage. Firstly, use symmetric encryption algorithms (such as AES) to encrypt data. This step mainly utilizes the high speed and efficiency of symmetric encryption for large-scale data encryption. Furthermore, using asymmetric encryption algorithms (such as RSA) to encrypt the symmetric encrypted key, this step mainly utilizes the security of asymmetric encryption for secure key transmission. Finally, store the encrypted data together with the encrypted key. When accessing data, use a private key to decrypt and obtain a symmetric key, and then use the symmetric key to decrypt and obtain the original data. This hybrid approach balances the security of data and the efficiency of encryption and decryption, making it particularly suitable for medical data storage scenarios with high requirements for data security and access efficiency. The fuzzy keyword search process incorporating attribute ciphertext is shown in Fig. 2.

Fig. 2
figure 2

Fuzzy keyword search process incorporating attribute ciphertext

In Fig. 2, The parameter setting stage provides the necessary initial values for the model, ensuring the correct execution of subsequent steps. Next, the keyword generation stage generates a set of keywords based on preset rules and algorithms. During the encryption phase, keywords are converted into ciphertext to ensure data security. Afterward, during the token generation phase, each user will generate a unique token for authentication and data access. During the search phase, users use tokens for queries, and the system matches them with ciphertext keywords and returns corresponding results. Finally, in the decryption stage, users use the obtained results and their own tokens to decrypt and obtain the final data. The entire process aims to achieve a safe and effective keyword search, taking into account both data security and availability. The security model of hybrid encryption algorithms is shown in Fig. 3.

Fig. 3
figure 3

A security model for hybrid encryption algorithms

In Fig. 3, The security model design covers two levels: data security and keyword search security. The goal of data security is to ensure that data is protected from unauthorized access, leakage, or tampering during storage, transmission, and processing. When designing a security model, adopting appropriate encryption techniques and access control mechanisms to maintain the confidentiality and integrity of data is crucial. Using a hybrid encryption algorithm, it combines the efficiency of symmetric encryption with the security of public key encryption. However, balancing the use of symmetric and asymmetric encryption to optimize performance remains a challenge. Keyword search security requires not exposing the details of encrypted data during the search process while protecting query privacy and keyword privacy. To this end, searchable encryption technology needs to be introduced to perform keyword searches while maintaining data encryption. Such a solution needs to protect data encryption and only leak necessary search results, while preventing keyword and encrypted data information leakage. In the latest technologies, zero-knowledge proof and homomorphic encryption have been introduced, further enhancing the security of data and the availability of search.

3.2 Privacy security information storage protection analysis algorithm

In the primeval setting stage of data, a detailed study of data storage and protection schemes is carried out, and solutions to ensure data privacy and security, as shown in Eq. 1.

$$\left\{\begin{array}{l}\sigma ={\widetilde{\sigma }}_{1}\widetilde{\beta }+{d}^{q+1}\widetilde{\beta }=\widetilde{\sigma }+{a}^{q+1}\\ e{\left(g,g\right)}^{{\sigma }_{1}}=e{\left(g,g\right)d}^{q+1}e{\left(g,g\right)}^{{\sigma }_{1}}\end{array}\right.$$

In Eq. (1), the public parameter is \(e{\left(g,g\right)}^{\sigma }\) and \(\widetilde{\beta }\) is the main key. \(a\) is the output value, and \(\sigma\) is the reconstruction key. When the key of the user attribute is generated, user properties do not qualify, as shown in Eq. (2).

$${r}{\prime}=\widetilde{r{\prime}}+\sum_{j\in \left[t\right]}{m}_{j}{d}^{q+1-j}$$

In Eq. (2), a set of keys belonging to a user attribute in the run phase \(r{\prime}\). Running the STK. KeyGen program at the same time is the process of generating the key by the mixed encryption algorithm, as shown in Eq. (3).

$$\left\{\begin{array}{l}{k}{\prime}={g}^{\frac{{\sigma }_{1}}{\alpha }}{m}^{\frac{r{\prime}}{{m}^{a}}}={g}^{\frac{{\sigma }_{1}}{\alpha }}{\left({g}^{d}\right)}^{\frac{\widetilde{r{\prime}}}{\alpha }}\prod_{j\epsilon \left[t\right]}{\left({g}^{{d}^{q+2-j}}\right)}^{{m}_{j}}\\ {k}_{0}{\prime}={g}^{r{\prime}}={g}^{\widetilde{r}{\prime}}\prod_{j\epsilon \left[t\right]}{\left({g}^{{d}^{q+1-j}}\right)}^{{m}_{j}}\end{array}\right.$$

In Eq. (3), \(k{\prime}\) and \({k}_{0}{\prime}\) are the attribute keys of some users, which help to determine the appropriate protection measures and levels. In symmetric encryption, encryption and decryption use the same key; in asymmetric encryption, the public key is used for encryption, and then the corresponding private key is used for decryption. In hybrid encryption, a random symmetric key may be generated to encrypt data, and then the recipient’s public key is used to encrypt this symmetric key.

There are some restrictions and precautions for key settings. For example, the key must be complex enough to prevent brute force cracking. The storage and transmission of keys also need to be secure to prevent theft. At the same time, if using symmetric keys, it is also necessary to find a secure way to share the keys. These are all factors that need to be considered when setting keys. For all attribute values, Eq. (4) can be obtained.

$${r}_{x}{\prime}={\widetilde{r}}_{x}{\prime}+r{\prime}\sum_{{i}{\prime}\in \left[u\right]}\frac{{b}_{{i}{\prime}}}{{R}_{x}-{\rho }^{*}\left({i}{\prime}\right)}$$

In Eq. (4), \(R\) is a parameter attribute, \(\rho\) and B is the access policy. key generation is usually a random generation process, and the generated key should be difficult to predict. \(r{\prime}\) denotes the challenge value of the challenge sequence. The key generation algorithm should be able to generate keys with sufficient strength, and the keys should be independent of each other, as shown in Eq. (5).

$${k}_{1,x}{\prime}={g}^{{r}_{x}{\prime}}={g}^{{\widetilde{r}}_{x}{\prime}}\prod_{{i}{\prime}\in \left[u\right]}{g}^{\frac{{\widetilde{r}}{\prime}{b}_{{i}{\prime}}}{{R}_{x}-{p}^{*}\left({i}{\prime}\right)}}\prod_{\left({i}{\prime},j\right)\in \left[u,t\right]}{g}^{\frac{{m}_{j}{b}_{i}{d}^{q+1-j}}{{R}_{x}-{\rho }^{*}\left({i}{\prime}\right)}}$$

In Eq. (5), \({k}_{1,x}^{i}\) is the attribute key of the remaining user. The user attribute key contains two parts: user identifier and identity attribute information. The identity attribute information is shown in Eq. (6).

$${v}^{{r}{\prime}}={v}^{{\widetilde{r}}{\prime}}\prod_{j\in \left[t\right]}{\left({g}^{{d}^{q+1-j}}\right)}^{{\widetilde{v}m}_{j}}\prod_{\left(j,i,k\right)\in \left[t,u,t\right]}{\left({g}^{\frac{{d}^{q+1+k-j}}{{b}_{i}}}\right)}^{{P}_{i,k}^{*}{m}_{j}}$$

In Eq. (6), OCS generates the protocol according to the user key, as shown in Eq. (7).

$${r}^{{\prime}{\prime}}-{\widetilde{r}}^{{\prime}{\prime}}+\sum_{j\in \left[t\right]}{m}_{j}{a}^{q+1-j}$$

Executing the OCS.KeyGen program is part of the key generation process, which generates a specific private key for each user to decrypt data encrypted through the public key. The generation of private keys is usually based on a set of attributes or credentials of the user, ensuring that only users who meet specific conditions can decrypt the corresponding data. When the program algorithm of OCS.KeyGen is executed, and the user attribute key generated is shown in Eq. (8).

$${r}_{x}={\widetilde{r}}_{x}+2\sum_{\left({i}{\prime},j\right)\in \left[u,t\right]}\frac{{b}_{{i}{\prime}}{m}_{j}{a}^{q+1-j}}{{R}_{x}-{p}^{*}\left({i}{\prime}\right)}+\sum_{{i}{\prime}\in \left[u\right]}\frac{{\widetilde{r}b}_{{i}{\prime}}}{{R}_{x}-{\rho }^{*}\left({i}{\prime}\right)}$$

In Eq. (8), \({r}_{x}\) belongs to a time-sensitive user identifier. Then, to store in ABF, the attributes are hashed, as shown in Eq. (9).

$${H}_{1}\left({R}_{e}\right),{H}_{2}\left({R}_{e}\right),\dots , {H}_{t}\left({R}_{e}\right)$$

In Eq. (9), \({H}_{t}\left({R}_{e}\right)\) is the encoded sequence of the index. Therefore, the index value is shown in Eq. (10).

$${s}_{1,e}\to {H}_{t}\left({R}_{e}\right)$$

In Eq. (10), \({s}_{1,e}\) is the index value. The ciphertext is sent to CSS, and ABF ensures the storage and transmission of medical data. Firstly, the study introduced the ABF filtering mechanism, which is an attribute-based filtering system that can perform filtering operations based on preset attributes, effectively preventing access requests from unrelated entities. The study applies this filtering mechanism to the storage and transmission process of medical data, ensuring that only entities with corresponding attributes (such as doctors and patients) can access relevant medical data by controlling access permissions. In addition, the study also introduced physical roles, system overview, and encryption techniques. Entity roles include data owners, data consumers, and cloud service providers, each with its own specific permissions and responsibilities. The system overview is a description of the entire system workflow, including the steps of data generation, encryption, storage, and decryption. Encryption technology is the core of protecting the security of medical data. The research adopts hybrid encryption technology, combining the advantages of symmetric and asymmetric encryption, which can effectively protect the security of data. In addition, the study also utilized searchable encryption technology, allowing data users to quickly find the medical data they need based on specific keywords while ensuring data security.

In the implementation of the ABF filtering mechanism, the information charts created by Tableau play a crucial role. This chart visually demonstrates how attribute-based filtering systems operate and how to prevent access requests from unrelated entities. The chart also reveals how symmetric encryption and asymmetric encryption are combined in hybrid encryption technology to achieve the optimal data protection effect. The specific technical details of the attribute encryption method are also shown in the chart. The attribute encryption method relies on the attributes of roles such as data owners, data consumers, and cloud service providers to encrypt medical data. Only entities with corresponding attributes can decrypt it. The application of searchable encryption technology enables data users to quickly find the required medical data without decrypting it. The visual display of these details makes the workflow of the entire system and the responsibilities of various entity roles clearer and also provides strong support for achieving the security protection of medical data. If DU needs medical information, it needs to be decoded. The initial decryption process is shown in Eq. (11).

$${H}_{1}\left(R\right)\to {s}_{t,R}$$

In Eq. (11), under certain circumstances, when R is not in ABF, outsourcing decryption operations need to be carried out, as shown in Eq. (12).

$$\widetilde{C}={e\left(m,g\right)}^{\frac{-rs}{\omega }}$$

In Eq. (12), \(\widetilde{C}\) is the intermediate value unrelated to generating the key. When decrypted again for this intermediate value \(\widetilde{C}\), the original medical data can be reproduced, as shown in Eq. (13).

$${sk}_{s}=\frac{{c}_{0}}{{\left(e{\left({c}_{1},k\right)}^{\frac{\alpha }{\omega }}\widetilde{E}\right)}^{tsk}}$$

In Eq. (13), \({sk}_{s}\) is the symmetric key. Combine user attributes and their location in the access matrix through a string. At the same time, it is embedded in the garbled ABF to hide the security analysis process of the access policy, as shown in Fig. 4.

Fig. 4
figure 4

Hide access policy security analysis process

In Fig. 4, the access policy cannot be obtained by malicious users. Therefore, in the security analysis of the access policy, the data anonymization technology can be used to protect privacy. Anonymous data can separate the user’s identity information and sensitive information from the user’s access policy so that malicious users can not directly obtain specific identity information or obtain specific privacy information. An access control mechanism is a common way to guarantee that only authorized users can obtain the relevant access. This method can effectively prevent malicious users from obtaining personal privacy information, and ensure that only authorized users can obtain personal privacy information according to this method. In the user access policy, this study uses encryption technology to protect personal privacy. On this basis, a cryptographic method is proposed, and the data in the cryptography is encrypted, so as to ensure that the information in the cryptography can only be decrypted and read by legitimate users. When performing security analysis, other security measures can also be applied, such as authentication, authentication, and firewalls, to improve the security of the system. This scheme can not only effectively prevent malicious users from stealing personal privacy but also provide more protection for personal data. Design and implement security audit and monitoring mechanisms to monitor the security of data storage and detect any unusual activity. This includes measures such as logging, alarm handling, anomaly detection, and regular security audits.

The security analysis of hybrid encryption schemes mainly examines their resistance to common attacks. For violent attacks, the symmetric encryption algorithm used in this scheme usually uses a sufficiently long key, which greatly increases the difficulty of brute force cracking; for critical recovery attacks and selective plaintext attacks, due to the introduction of asymmetric encryption algorithms, even if the attacker obtains the encrypted key and a portion of plaintext and ciphertext pairs, it is difficult to recover the complete key or carry out effective attacks. Therefore, the hybrid encryption scheme has strong robustness. In addition, this scheme ensures the ability of sensitive medical data to be protected from unauthorized access through asymmetric encryption of keys, greatly improving the security of medical data.

3.3 Incorporate the privacy security storage protection design of private medical information with mixed encryption

Ensure that patients’ private medical information is protected during storage and transmission to prevent unauthorized visitors from obtaining and tampering with this sensitive data. After implementing hybrid encryption and secure storage, access control and authentication mechanisms are further utilized to ensure that only authorized users have access to private medical information. When these authorized users access data, they must go through strict authentication and permission confirmation to prevent illegal intrusion and data tampering [18, 19]. Endpoint detection and response (EDR) is also applied, which monitors and collects various information on each terminal (such as computers and mobile phones), and then analyzes it to detect, prevent, and respond to threats to these devices. EDR can effectively identify complex and advanced threats [20,21,22]. To solve the issue of low encryption security of medical electronic medical record data, this study combines Symmetric key Algorithm (SKA) and Asymmetric Cryptographic Algorithm (Asymmetric Cryptographic Algorithm) [23, 24]. An enhanced hybrid encryption method for medical data based on SKA and ACA is proposed, as shown in Fig. 5.

Fig. 5
figure 5

A hybrid encryption method for medical data enhancement based on SKA and ACA

In Fig. 5, the sender requests access to medical data. After the authorization is approved, the SKA key is used to encrypt the medical data in plain text and obtain the ciphertext. The sender then encrypts the SKA essential data with the ACA’s public key. The encrypted public key is obtained, and the mixed information of the encrypted ciphertext and the encrypted public key is sent simultaneously. Finally, after receiving the mixed information, the receiver applies EPNRSA’s private key to decrypt the encrypted public key and get the SKA key. Then, the SKA key is used to decrypt the encrypted ciphertext to get the plaintext of the medical data. The flowchart for the design of privacy security storage protection of private medical information incorporating mixed encryption is shown in Fig. 6.

Fig. 6
figure 6

Design flowchart for privacy and secure storage protection of private medical information integrated with mixed encryption

In Fig. 6, an individual’s medical data is first collected and prepared for password processing. The ACA is then used to pre-encrypt the collected medical information, converting sensitive individual medical data into ciphertext. SKA is adopted on the basis of pre-encryption. Through secondary encryption, not only improves the confidentiality of information but also greatly reduces the time required for encryption and decryption. The medical data after the double password is securely stored in the cloud. Individual users and legally recognized medical organizations may be provided with an encrypted private key or key to enable them to access and decrypt data if necessary. Legitimate users can decrypt the data when necessary, thus ensuring the privacy of the data and improving the efficiency of data utilization. Implement the whole process of data security monitoring, and timely detection and processing of potential security risks to reduce the risk of data leakage. This data protection strategy is not limited to mixed encryption and stored procedures but also includes a complete data security monitoring mechanism. From information collection to encrypted transmission, cloud storage, to data extraction, decryption, and eventual deletion, real-time security monitoring is carried out at every step, recording and analyzing all system behaviors to ensure the auditability of the entire process. Protected data is monitored throughout its life cycle [25,26,27]. The performance evaluation of hybrid encryption schemes mainly includes indicators such as encryption/decryption time, storage overhead, and computational complexity. Specifically, symmetric encryption algorithms such as AES can achieve high-speed encryption/decryption on on hardware, while asymmetric encryption algorithms such as RSA can ensure the security of key transmission, despite their relatively high computational complexity. Although the storage cost may slightly increase due to the need to store encrypted data and keys, in large-scale data storage, this part of the cost is relatively small. Compared to schemes that only use symmetric or asymmetric encryption, hybrid encryption schemes ensure data security while more effectively balancing encryption and decryption speed and computational complexity, demonstrating their advantages in medical data storage.

4 Analysis of personalized medical data privacy security storage protection based on hybrid encryption

The storage protection mode based on hybrid encryption technology provides a new possibility to solve this problem. Hybrid encryption by combining public key encryption and private key encryption, strengthens the security of data, ensures the privacy of data, and balances the encryption strength and efficiency. Multiple factors and challenges need to be considered to form a more comprehensive, scientific solution.

4.1 Calculation cost and storage consumption analysis

For the encryption of public medical data sets, the efficient hybrid encryption mode is mainly adopted. First, asymmetric encryption technology is used to transcode all public medical data into ciphertext that cannot be read directly to provide basic data security. Then, the symmetric encryption technology is used to encrypt the asymmetric ciphertext twice, which further improves the data security. Table 1 displays the parameter settings of the model.

Table 1 Simulation model parameters

In Table 1, under the hardware environment of 64-bit Win11 operating system, i5-7500 CPU, 16 GB memory, using Eclipse software and Java development language, as well as the open source class library of jpbc2.0.0, simulate the experimental environment. Conduct experiments using the recognized benchmark medical dataset MIMIC-III. In order to ensure the reliability of the results, 100 replicates were conducted for each experiment, and the final results were compared with the average value. The consumption comparison of the user attribute key store is shown in Fig. 7.

Fig. 7
figure 7

Comparison of consumption of user attribute key storage

In Fig. 7, a prime order bilinear group is adopted, Z is the prime order bilinear group, Q is the length of the user attribute list. User attribute privacy key store consumption increases with the number of user attributes. At the same time, the consumption of hybrid encryption privacy storage technology is much smaller than that of traditional schemes. The time consumption of decrypting 100 KB and 300 KB plaintext medical data was compared for the user’s acquisition of medical data.

In Fig. 8, due to the adoption of key outsourcing, the decryption time required by this method is significantly shortened compared with other methods under the same file size. The scheme gives full play to the advantages of key outsourcing, and moves the decryption of the key to the cloud, thus reducing the computing load of the user on the local device and reducing the energy consumption of network transmission. In our MD size of 100 KB, as the number increases, the required number of milliseconds is the lowest among the three plaintext decryptions. In the Our MD size of 3000 KB, there is a trend towards approaching Our MD size of 100 KB as the quantity increases. In Test Our MD size 100 KB, as the quantity increases, the required processing time reaches 1200 ms at 30. This algorithm not only has a certain degree of security, but also has a high computational efficiency, and has obvious advantages in large-scale data encryption and decryption applications. Compared with other distributed encryption methods, this method distributes the data and calculation to the local and cloud, which can ensure the efficiency of calculation and the security of data, thus greatly reducing the time required for decryption under the same file size. Therefore, the proposed method has great advantages for the analysis and processing of massive medical information.

Fig. 8
figure 8

Time consumption for users to obtain medical data

4.2 Privacy protection and risk analysis

After understanding the computational costs and storage consumption required for privacy protection of medical data, further exploration shifts to the consideration of privacy protection itself and its potential risks. Section 4.2 will delve into this topic in depth. This study not only needs to maintain the confidentiality of data, but also needs to balance the potential risks, including data leakage, illegal access, and tampering. Therefore, a comprehensive evaluation and risk analysis of privacy protection strategies is crucial. Through simulation experiments, the time consumption between this scheme and other schemes is analyzed and compared. The simulation experiment environment is shown in Table 2.

Table 2 Simulation experimental environment

In Table 2, the 64-bit Win10 operating system, i5-4490CPU, and 8 GB memory are used to simulate the experimental environment. Use Flutter software, use C +  + development language and SQLite open-source class library. In this simulation experiment, Flutter software was chosen, which has cross-platform characteristics that enable it to run in different operating system environments. As a development language, C +  + ensures the smooth progress of experiments with its efficient execution speed and powerful performance. Meanwhile, SQLite’s open-source class library has been adopted, which is a lightweight database that does not require configuration and can be directly embedded into programs, greatly simplifying the complexity of data management. These three jointly constructed the operating environment of the experiment, ensuring the accuracy and reliability of the experiment. Personal health information storage security mechanism based on hybrid encryption technology, first of all, ACA ensures that personal medical information can only be decrypted by users with private keys, so as to avoid illegal access. Secondly, SKA is adopted to ensure the safety of data and ensure the preservation and transmission of information. On the other hand, key outsourcing decryption does not need to send private keys and original data, thus reducing the risk of theft and interception. The comparison diagram of user privacy information disclosure is shown in Fig. 9.

Fig. 9
figure 9

Comparison chart of user privacy information leakage

In Fig. 9, While enhancing medical information security, it also faces some security risks. On the one hand, if a user’s password is stolen or lost, the password data will not be restored, and the original data may also fall into the hands of an attacker. On the other hand, adopting hybrid encryption technology requires a stable connection between the client and cloud computing. If the connection is unstable, it may lead to decryption failure and even data loss. In addition, although key outsourcing decryption can effectively prevent the outflow of private keys and data, security control and management in cloud computing environments are equally important. Once attacked, information within the cloud may be leaked. This is consistent with the results shown in the experimental data: after filtering for 100 ms, the acceleration fluctuated from 0.07–0.26 g to 0.12–0.31 g. This more stable data transmission can reduce information security risks to some extent, but there are still potential hazards. The attack frequency of cloud servers is compared, as shown in Fig. 10.

Fig. 10
figure 10

Cloud server attack frequency comparison chart

In Fig. 10, the yellow curve in Fig. 10a represents the test data, while the blue curve represents the actual data collected. With the increase in data volume, both test data and real data showed a temporary trend of decreasing attack frequency. However, after the data volume of 730–780, the attack frequency increased, but the effect of the test data was better than the real data. The rose red curve in Fig. 10b is the true value, while the blue curve is the test data. As the K value increases, it hovers around 60% of the attack frequency. Based on the above factors, for the application of a personalized medical data storage protection scheme based on hybrid encryption, the security awareness of users and cloud service providers should be strengthened, key management should be carried out, and cloud servers should be updated and maintained in a timely manner to minimize risks. The comparison between the privacy secure storage design model and SoTA is shown in Fig. 11.

Fig. 11
figure 11

Comparison between privacy secure storage design model and SoTA

In Fig. 11, the accuracy changes of the training set for the privacy secure storage design model and the SoTA model are shown. As the number of iterations increased from 0 to 600, the accuracy of the training set increased from the lowest 0.53 to 0.96, demonstrating a significant performance improvement. Similarly, the accuracy of the test set has increased from the lowest 0.64 to 0.99, demonstrating that the model can achieve excellent testing performance after sufficient iterations. This supports the effectiveness of the model in designing privacy and secure storage.

5 Conclusion

Aiming at the problem of security storage protection of personalized medical data privacy with mixed encryption, this study adopts a model based on mixed encryption and verifies various indicators. The yellow curve represents the test data, while the blue curve represents the actual data received. When the amount of data becomes larger and larger, whether it is test data or real data, it will show a temporary trend of being attacked less frequently. However, after the data volume of 730–780, the attack frequency will increase, but the effect of the test data is better than the real data. Although key outsourcing decryption can effectively prevent private key and data outflow, in cloud computing, private information may still be leaked under attack. The purple curve represents the Test Our MD size of 100 KB. As the number increases, the processing time first reaches 1200 ms at 30 ms. This method can not only ensure security, but also improve the operation speed, and is suitable for large-scale data encryption and decryption. The combination of ACA and SKA ensures the confidentiality and transmission security of information, while the use of key outsourcing decryption technology reduces the risk of private keys and data theft. However, the operation of this solution requires a stable cloud computing environment, and if cloud services are attacked or connectivity is unstable, it may lead to data leakage or loss. Therefore, when implementing this solution, in addition to strengthening user security awareness and key management, cloud service providers also need to conduct regular server maintenance and updates to ensure data security. To enhance the security of encryption schemes, it is necessary to comprehensively evaluate potential vulnerabilities and develop countermeasures. Possible attack vectors include brute force cracking of keys, edge channel attacks, etc. Therefore, it is recommended to take measures such as increasing the length of the key and increasing the complexity of the key to increase the difficulty of cracking. At the same time, defense mechanisms such as random delay or noise interference are introduced to resist edge channel attacks. These additional security measures will effectively reduce the risk of encryption schemes. In practical applications, in addition to mixing encrypted personalized medical data, cost is also a crucial consideration. For future work, research should provide practical suggestions and explore how to consider cost factors to optimize the application of hybrid encryption in personalized healthcare, further improving the efficiency and security of medical data privacy and secure storage.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Lv, J. RETRACTED ARTICLE: Research on privacy and secure storage protection of personalized medical data based on hybrid encryption. EURASIP J. on Info. Security 2024, 1 (2024).

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