ISO/IEC 38505-1:2017 情報技術— ITのガバナンス—データのガバナンス—パート1：データのガバナンスへのISO / IEC38500の適用 | ページ 6

3.1

anonymization

process by which personally identifiable information (PII) is irreversibly altered in such a way that a PII principal can no longer be identified directly or indirectly, either by the PII controller alone or in collaboration with any other party

[SOURCE:ISO/IEC 29100:2011, 2.2]

3.2

big data

data set(s) with characteristics (e.g. volume, velocity, variety, variability, veracity, etc.) that for a particular problem domain at a given point in time cannot be efficiently processed using current/existing/established/traditional technologies and techniques in order to extract value

Note 1 to entry: The term Big Data is commonly used in many different ways, for example as the name of the scalable technology used to handle big data extensive datasets.

[SOURCE:ISO/IEC 20546:— ¹ , 3.2.1]

3.3

cloud computing

paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on-demand

Note 1 to entry: Examples of resources include servers, operating systems, networks, software, applications, and storage equipment.

[SOURCE:ISO/IEC 17788:2014, 3.2.5]

3.4

data accountability

accountability for data and its use

Note 1 to entry: The “use” of data includes all activities associated with data.

3.5

de-identification

general term for any process of removing the association between a set of identifying data and the data subject

[SOURCE:ISO/TS 25237:2008, 3.18]

3.6

internet of things

IoT

global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies

Note 1 to entry: Through the exploitation of identification, data capture, processing and communication capabilities, the IoT makes full use of things to offer services to all kinds of applications, whilst ensuring that security and privacy requirements are fulfilled.

Note 2 to entry: In a broad perspective, the IoT can be perceived as a vision with technological and societal implications.

[SOURCE:Rec. ITU-T Y.2060]

3.7

machine learning

process using algorithms rather than procedural coding that enables learning from existing data in order to predict future outcomes

3.8

pseudonymization

process applied to personally identifiable information (PII) which replaces identifying information with an alias

Note 1 to entry: Pseudonymization can be performed either by PII principals themselves or by PII controllers. Pseudonymization can be used by PII principals to consistently use a resource or service without disclosing their identity to this resource or service (or between services), while still being held accountable for that use.

Note 2 to entry: Pseudonymization does not rule out the possibility that there might be (a restricted set of) privacy stakeholders other than the PII controller of the pseudonymized data which are able to determine the PII principal’s identity based on the alias and data linked to it.

[SOURCE:ISO/IEC 29100:2011, 2.24]

3.9

personally identifiable information

PII

any information that (a) can be used to identify the PII principal to whom such information relates, or (b) is or might be directly or indirectly linked to a PII principal

Note 1 to entry: To determine whether a PII principal is identifiable, account should be taken of all the means which can reasonably be used by the privacy stakeholder holding the data, or by any other party, to identify that natural person.

[SOURCE:ISO/IEC 29100:2011, 2.9]

3.10

PII principal

natural person to whom the personally identifiable information (PII) relates

Note 1 to entry: Depending on the jurisdiction and the particular data protection and privacy legislation, the synonym “data subject” can also be used instead of the term “PII principal”.

[SOURCE:ISO/IEC 29100:2011, 2.11]