ISO/IEC TR 23186:2018

TECHNICAL ISO/IEC TR
REPORT 23186
First edition
2018-12
Information technology — Cloud
computing — Framework of trust for
processing of multi-sourced data
Reference number
©
ISO/IEC 2018
© ISO/IEC 2018
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ii © ISO/IEC 2018 – All rights reserved

Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Symbols and abbreviated terms . 2
5 Scenarios . 2
5.1 Using multi-sourced data to reduce traffic deaths and injuries . 2
5.2 Using multi-sourced data for home automation . 3
5.3 Using multi-sourced data for automotive operations . 4
6 Trust . 5
7 Data access and processing rights . 6
8 Framework for trusted processing of multi-sourced data . 7
8.1 Introduction . 7
8.2 Data flow . 7
8.3 Elements of trust . 8
8.3.1 General. 8
8.3.2 Data use obligations and controls . 8
8.3.3 Data provenance records, quality and integrity .10
8.3.4 Chain of custody .11
8.3.5 Security and privacy .11
8.3.6 Immutable proof of compliance.11
9 Using the framework in agreements .12
9.1 General .12
9.2 Data use obligations and controls .12
9.3 Data provenance records, quality and integrity .12
9.4 Chain of custody .12
9.5 Security and privacy .12
9.6 Immutable proof of compliance .12
Annex A (informative) Data use obligations and data use controls .13
Bibliography .15
© ISO/IEC 2018 – All rights reserved iii

Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that
are members of ISO or IEC participate in the development of International Standards through
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take part in the work.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for
the different types of document should be noted. This document was drafted in accordance with the
editorial rules of the ISO/IEC Directives, Part 2 (see www .iso .org/directives).
Attention is drawn to the possibility that some of the elements of this document may be the subject
of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent
rights. Details of any patent rights identified during the development of the document will be in the
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list of patent declarations received (see http: //patents .iec .ch).
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.org/iso/foreword .html.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 38, Cloud Computing and Distributed Platforms.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www .iso .org/members .html.
iv © ISO/IEC 2018 – All rights reserved

Introduction
There are many business and technical aspects relating to the processing of multi-sourced data, but
trust between cloud service users, cloud service customers and the cloud service provider(s) is a
significant market issue.
Cloud processing of multi-sourced data is in its early stages of development in the industry, and it is
anticipated that specific customer requirements will differ and will evolve over time. Industry clouds
have begun to form, and in some cases, their primary purpose is to bring multi-sourced data together
from participants in specific industry or community sectors to achieve common objectives. Trust may
be required in these scenarios because of regulations, agreements or policies attached to the data.
Processing of multi-sourced data will be essential to artificial intelligence applications along with
machine learning on financial, transportation, energy, manufacturing, agricultural and government
data. Trust in the data, in the cloud service provider(s), in the processing functions, in the outcomes and
among the parties is essential to the success of these projects.
The elements of trust described in this report pertain to Personally Identifiable Information (PII),
Organizational Confidential Data (OCD) or any other kind of data that can be a part of multi-sourced data.
© ISO/IEC 2018 – All rights reserved v

TECHNICAL REPORT ISO/IEC TR 23186:2018(E)
Information technology — Cloud computing — Framework
of trust for processing of multi-sourced data
1 Scope
This document describes a framework of trust for the processing of multi-sourced data that includes
data use obligations and controls, data provenance, chain of custody, security and immutable proof of
compliance as elements of the framework.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies. For
undated references, the latest edition of the referenced document (including any amendments) applies.
ISO/IEC 17788, Information technology — Cloud computing — Overview and vocabulary
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 17788 and the
following apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at http: //www .iso .org/obp
— IEC Electropedia: available at http: //www .electropedia .org/
3.1
chain of custody
demonstrable possession, movement, handling, and location of material from one point in time
until another
[SOURCE: ISO/IEC 27050-1:2016, 3.1]
3.2
data
recorded information
[SOURCE: ISO 22005:2007, 3.11]
3.3
data processing
systematic performance of operations upon data
[SOURCE: ISO 2382:2015, 2121276, modified — Notes 1 to 4 to entry have been deleted and the alternate
term “automatic data processing” has been deleted.]
3.4
data set
logically meaningful grouping of data
[SOURCE: ISO 8000-2:2018, 3.2.4, modified — EXAMPLES 1 and 2 have been deleted.]
© ISO/IEC 2018 – All rights reserved 1

3.5
multi-sourced data
data that consists of separate data sets that have been generated by multiple, diverse sources and
assembled by one or more cloud services from one or more CSPs
Note 1 to entry: The data sets are then subject to combined analysis and processing with the aim of extracting
insights and information not obtainable through analysis of each dataset on its own.
3.6
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
[SOURCE: ISO/IEC 29100:2011, 2.9, modified — The NOTE has been deleted.]
3.7
trust
degree to which a user or other stakeholder has confidence that a product or system will behave as
intended
[SOURCE: ISO/IEC 25010:2011, 4.1.3.2]
4 Symbols and abbreviated terms
PII Personally identifiable information
5 Scenarios
5.1 Using multi-sourced data to reduce traffic deaths and injuries
Worldwide, 1,25 million people die each year from traffic-related accidents and between 20 million
and 50 million people suffer injuries. Data sets include accident data, roadway attributes, land use,
demographics, commuting patterns, parking violations and existing safety improvements. One of the
key outcomes is an "exposure model" that predicts the number of cars in a given location at a given
time. Actual measurements of traffic are very expensive while predictions using machine learning are
relatively inexpensive.
For example, In the US, where 34,000 people die annually in traffic-related accidents, a non-profit
1)
organization, called DataKind® is using data and machine learning to develop models to predict traffic
accident patterns. These patterns can then be used to determine where to focus street improvements
and predict the effect on accident rates for specific improvements. Street improvements have included
traffic signals and controls, bicycle lanes, road design and treatments.
2)
DataKind® held a DATADIVE® to bring data scientists together to transform the available data and
develop the model.
One of the key challenge
...