## 4 ) Individuals: “Personal data” involving attributes

Personal data include personal attributes, movement/action/consumption history, wearable device data and other personal information, as well as human traffic and product information that has been processed to prevent specific persons from being identified. Therefore, this chapter interprets “personal data” as including not only personal information as clearly defined by law but also other personal related information having close to law-defined personal information.

### 2. Progress in Data Distribution and Utilization

This paragraph focuses on progress in data distribution and utilization and ongoing structural changes toward the advent of the age of big data utilization.

## (1) Explosive Expansion in Data Distribution Volume

Network data traffic volume has dramatically increased. As for data traffic in Japan, broadband service (FTTH/DSL/CATV/FWA) users' total download traffic has rapidly increased since 2014. The latest available year-on-year growth came to 52%. Japan's mobile communication traffic has scored a 1.3-fold annual increase, with total download traffic growing 35% year on year.

## (2) Progress in Technological Innovation to Increase Data Processing Speed

Digital network development and IoT progress through smaller and cheaper IoT devices such as smartphones and sensors have allowed big data to be efficiently collected and shared. Furthermore, cloud computing has allowed even low-capacity computers to conduct massive data processing, accelerating the creation of added values and innovation through data utilization.

Data flow through the centralization and decentralization of data has made progress in promoting this trend more efficiently. Data centralization includes machine and deep learning of big data aggregated on a cloud-based environment, representing a typical data intensive society in which the aggregation of high-quality data for learning leads to a competitive advantage. Data decentralization mobilizes not only cloud computing but also edge computing using deep learning to process data in an optimum manner meeting value density in preparation for solving the problem of massive data in the IoT age (Figure 2-1-2-1).

## (3) Diversification of Data Categories

As shown above, data are classified into four broad categories. A further breakdown of the four categories indicates very various data. Recently, structuralized data have systematically increased and been used for discovering new scientific knowledge and creating new businesses. In the future, various and massive non-structuralized data will be accumulated on a real-time basis and connected with each other through networks in line with IoT development. It is expected that these data will be analyzed to dramatically change social systems.

Such expectation is behind the open data policies for opening public sector data, which have made progress

Figure 2-1-2-1 Data centralization and decentralization

(Source) "Survey Research on Secure/Safe Data Distribution and Utilization," MIC (2017)

Figure 2-1-2-2 Data traffic and storage cost trends

(Source) "Survey Research on Secure/Safe Data Distribution and Utilization," MIC (2017)