###### Abstract  
Currently, a large amount of biological information is accumulated, such as the area of genome sequencing as well as high-precision biometric information stored in wearable terminals and a growing database of health, medication, and medical information. The development of AI (artificial intelligence) and machine learning has increased its analytical power overwhelmingly. It is becoming more difficult to take measures against the leakage of personal information, and it is becoming difficult to determine privacy risks in advance. In this paper, we review those problems and propose a new method of managing private data. To solve such problems, we look at concepts of dynamic consent and privacy agents, which are drawing growing interest. In particular, efficient and broadly applicable technical means to support such concepts have been proposed. We considered using the current cloud platforms as an effective solution to this problem. We designed an architecture named Docker Vectorization and carried out a comprehensive analysis of the demand and feasibility of such a system in an era of increasing privacy management complexity. We believe we provided sufficient explanations for why Docker Vectorization of privacy agents in the cloud will be a powerful tool for providing sustainable and scalable privacy controls for data subjects.  
genome; privacy; standard; big data; Docker; container +
Footnote †: journal: Applied Sciences  
analyzing data, it is desirable to increase the data shared by minimizing such concerns and risks.  
Especially in genome information processing, there is a rapid development of the technologies and several ongoing standardization activities [2; 3; 4]. National HER (nation-scale electronic health records) is also considered in Japan [5]. However, there is also a strong need for privacy protection which also can be seen in the development of regulations [6; 7; 8; 9; 10]. There is substantial concern about the privacy of genomic information [11; 12; 13], and there are related studies on privacy risks and protection methods [14; 15; 16]. As a method to handle the uncertainty of privacy risk, the concept of dynamic consent was proposed and tested in previous studies [17; 18]. Those relevant activities and reports show an urgent and strong need to use genome data with an optimal balance of risks and benefits, as we reported in several relevant papers [19; 20].  
In this paper, we primarily focus on the support of dynamic consent. In the field of medical information, the need for dynamic consent, which can dynamically change the permission in accordance with the analysis technology that advances day by day, has been recognized.  
Therefore, [PERSON] proposed a personal AI agent that collects, manages, and protects personal data [21]. Personal AI agents manage the personal data of the data subject and the terms of use on behalf of the data subject. By allowing personal AI agents to act on behalf of data subjects for advanced judgment, the conditions for the use of personal data are better than the conventional framework. It is an attractive proposal because it can be specified flexibly.  
However, these ideas are about the goals of privacy management, and the means to technically protect such privacy management have not been discussed so far. Therefore, we would like to propose Docker vectorization.  
Docker vectorization proposes the same idea from the aspect of the concrete implementation method, which consists of the following three main points:  
1. Realized by cloud and Docker container mechanism;
2. Include dynamic consent in the provision of the analyzed result;
3. Provide multilateral security (zero trust security).  
In this report, the feasibility, merits, and issues of 1-3 are discussed in more detail.