Docker Vectorization, a Cloud-Native Privacy Agent—The Analysis of Demand and Feasibility for Era of Developing Complexity of Privacy Management

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.

Keywords

• genome
• privacy
• standard
• big data
• Docker
• container