# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 2 Materials and Methods
### Implementation Strategy
#### 2.1.1 AC Policies  
The first step in analyzing approaches to AC is considering the representation and implementation of the policies. AC policies are comprised of rules that are typically organized in a hierarchical relationship. The rules define what data users are permitted to access and what actions they are allowed to execute. Privacy requirements based upon Federal laws such as the Health Insurance Portability and Accountability Act (HIPAA) ([[https://www.hhs.gov/hipaa](https://www.hhs.gov/hipaa)]([https://www.hhs.gov/hipaa](https://www.hhs.gov/hipaa)) accessed on 14 January 2023) and industry regulations, such as the California Consumer Privacy Act (CCPA) ([[https://oag.ca.gov/privacy/ccpa](https://oag.ca.gov/privacy/ccpa)]([https://oag.ca.gov/privacy/ccpa](https://oag.ca.gov/privacy/ccpa)) accessed on 14 January 2023) drive the rules at the top of the hierarchy. For example, data that is considered PII or Protected Health Information (PHI) are subject to such legal requirements. Data providers or users, i.e., the data consumer or data owner, also have expectations of data protection and privacy that are translated into rules. The AC rule hierarchy levels are generally as follows:  
1. Government regulations;
2. Industry-specified directives (e.g., conflicts of interest);
3. Consumer (data owner) specified directives;
4. Consumer-proxy specified directives. Translating human-language-based data governance policies into digital language can be a significant implementation challenge for complex, dynamic AC policies. [PERSON] et al. describe the development of LEGALEASE and GROK for building and operating a system to automate governance policy definitions [10]. Human-language privacy policies are implemented in a MapReduce-like big data system. How user data flow among the systems is tracked. Bringing together teams that might not directly interact to define how legal policies are implemented in computer code can be time-consuming. Adding to this cost is the periodic check auditors need to perform to ensure the code continues to comply with the policies. The proposed framework helps automate and speed up the process of defining the data attributes used to control where the data are stored, who can access the data, and for what purpose. Lawyers and privacy personnel encode their policies using the LEGALEASE logic language and then using the GROK mapper, identify the code that might be affected by the privacy policies. Privacy managers can then work with developers, for example, to update only the portion of the code that is affected. This enables more focused updates on the code and data flows to ensure compliance with security policies. The prototype was implemented at a limited scale, and thus there is a need to test the expansion to large complex policies implemented on systems of multiple data-sensitivity classification levels. [PERSON] et al. define a framework for verification of the security guidelines [11]. This covers the software development lifecycle, including the secure coding practices of the Open Web Application Security Process (OWASP) ([[https://owasp.org/www-project-secure-coding-practices-quick-reference-guide/](https://owasp.org/www-project-secure-coding-practices-quick-reference-guide/)]([https://owasp.org/www-project-secure-coding-practices-quick-reference-guide/](https://owasp.org/www-project-secure-coding-practices-quick-reference-guide/)) accessed on 14 January 2023). In support of AC, the data processing lifecycle is managed using labels and verification checks. Considering the healthcare use case, requirements for challenging dynamic fine-grain AC policies could present a variety of issues. Fine-grain AC is the ability to control individual subject access to objects, where subjects have different levels of privileges and objects are at multiple classification or sensitivity levels. Fine-grain AC enables more precise control over who has access to which data under dynamic environmental circumstances.