# A Hyperledger Fabric-Based System Framework for Healthcare Data Management
## 2 Literature Review  
### Privacy in Healthcare Domains  
The definition of privacy is different in different contexts. This paper adopts the views of [PERSON] and [PERSON] (2019), that is, healthcare data privacy violations occur when the wrong actor can access the information; the process of accessing the information violates laws and regulations; or the purpose of the access is inappropriate [9]. The violation of privacy will cause concerns both on personal interests and social ethics.  
There have been various attempts to address privacy issues. From the legislative aspect, healthcare privacy in Australia is protected by a combination of Commonwealth, State, and Territory legislation. The Privacy Act 1988 (Privacy Act) regulates the privacy legal obligations that all of the health service providers in the Commonwealth public sector and the national private sector must comply with to protect their patient's health information. Usage, disclosure, modification, and destruction of health records are prescribed in the Australian Privacy Principles (APPs). Most Australian states and territories have equivalent privacy jurisdictions that apply to public and private sector healthcare providers [10].  
More legislations have been established to further strengthen the governance of privacy protection of electronic health records. These include the Healthcare Identifiers Act 2010 (HI Act), the My Health Records Act 2012, the My Health Records Rule 2016, and the My Health Records Regulation 2012. These legislations and regulations set out privacy requirements for collecting, using, and disclosing personal information to operate and manage the My Health Record system [11].  
From a technology standpoint, researchers developed various privacy-preserving techniques that were aimed at privacy concerns for individuals. Data anonymization techniques, such as data masking [12] and pseudonymization [13], are used to protect private and sensitive information. Data perturbation modifies the original dataset by adding random noise to ensure the statistical properties, but it is generally at the cost of destroying the authenticity and integrity of the original data [14]. Data generalization deliberately removes some of the data to make it less identifiable while retaining a measure of data accuracy. Among noise-based approaches, the most representative method is differential privacy [15] which aims to remove individual characteristics while preserving statistical characteristics to protect user privacy. To solve problems of privacy disclosure and data publishing, the \(k\)-anonymity anonymizes certain attribute values in the dataset to ensure that at least \(k\)\(-\)1 pieces of the personal information that are contained in the public data cannot be determined by other information [16]. The \(l\)-diversity extended the \(k\)-anonymity model by considering the constraints on sensitive attributes; \(t\)-closeness ensures the distribution of sensitive information does not exceed the threshold \(t\)[17]. Another approach is to encode or encrypt the original data through cryptographic methods before calculation, such as garbled circuit [18], homomorphic encryption [19], and secret sharing [20]. The secure multi-party computation (MPC) can obtain data use-value without revealing the original data content [21]. The zero-knowledge proof (ZKP) disallows the verifier from obtaining any additional information other than the result of the judgment [22].  
In the privacy-preserving domain, the biggest loophole lies in people's illegal behavior. What needs to be done is to develop more solutions at the technical level, thereby reducing the cost of legal supervision and the cognitive cost of users. In this regard, technical measures must interact with legal requirements as well as economic incentives to improve the situation.