# Healthchain: A Privacy Protection System for Medical Data Based on Blockchain
## 2 Related Work  
Today, with increasingly advanced technology, healthcare, as a huge industry closely related to everyone, is highly valued by governments and enterprises all over the world. Companies interested in healthcare not only include traditional medical companies, but also technology giants, such as Microsoft and Google. Especially in the current global background of the raging COVID-19 pandemic, people are increasingly placing high hopes in e-health, which has seemingly unlimited potential for development [3]. In response to a global pandemic like COVID-19, the source of medical data will no longer be limited to medical institutions, but will rely on mobile devices, including wearable devices [4]. The health data collected independently by the user is used as a direct digital data source to ensure the reliability, integrity, security, attribution, and auditability of the data. It helps to improve the overall assessment and monitoring of patients as individuals [5]. In addition, in order to better integrate and make full use of these large amounts of fragmented data, machine learning and artificial intelligence algorithms are used to build AI models that can predict COVID-19 symptoms, understand how it spreads, and speed up the use of medical data research and treatment [6]. These solutions have encountered and solved the problem of data source credibility, the problem of data sharing caused by mutual distrust between enterprises, and the problem of how to integrate and utilize a large amount of fragmented data. At the same time, they also encountered another common problem, that is, the problem of data privacy protection. It can be seen that in the future digital medical field, the issue of data privacy will be a major and inevitable issue. Many solutions have been proposed for the privacy protection of medical data. In the data acquisition stage, traditional privacy protection schemes are mostly based on anonymous technology. The fundamental idea is to hide the relationship between individuals and data. However, if we are only deleting personal attribute information, it is still possible for individuals to be identified [7], which is not conducive to data sharing in the scenario of electronic medical records. Thus, k-anonymity [8] was proposed, followed by a series of improvements [9; 10; 11]. However, such schemes often rely too much on assumptions of the attackers' background knowledge, and cannot provide rigorous and effective proof to users regarding their level of privacy protection. Therefore, differential privacy technology [12] has been introduced into this field. For the data storage stage, the existing solutions are mostly based on cloud platforms for data storage. For example, in 2014, [PERSON] et al. [13] proposed a design scheme for a data privacy security framework for electronic health systems which are based on web services. This scheme limits the access rights of different types of users and encrypts the files before they are uploaded to the cloud in order to protect data privacy. Similarly, [PERSON] et al. [14] proposed a framework based on ABE, which allows the secure outsourcing of computationally intensive data decryption processes to cloud servers. This reduces the time required for decryption on the user's side, and reduces the computing power required for users to access the data. Many encryption schemes (such as [15; 16; 17]) and audit schemes [18; 19; 20] have been proposed to solve data confidentiality and integrity problems in the data storage process, but how to select the appropriate auditors is a problem that still needs to be solved. At the same time, this approach relies heavily on the reliability of service providers and applications. If the third party is malicious, the effectiveness of these encryption schemes and audit schemes is compromised.