Information technology — Cloud computing — Framework of trust for processing of multi-sourced data

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.

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18-Dec-2018
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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 TR 23186:2018(E)
ISO/IEC 2018
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ISO/IEC TR 23186:2018(E)
COPYRIGHT PROTECTED DOCUMENT
© ISO/IEC 2018

All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may

be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting

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Published in Switzerland
ii © ISO/IEC 2018 – All rights reserved
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ISO/IEC TR 23186:2018(E)
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

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ISO/IEC TR 23186:2018(E)
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

technical committees established by the respective organization to deal with particular fields of

technical activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other

international organizations, governmental and non-governmental, in liaison with ISO and IEC, also

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

Introduction and/or on the ISO list of patent declarations received (see www .iso .org/patents) or the IEC

list of patent declarations received (see http: //patents .iec .ch).

Any trade name used in this document is information given for the convenience of users and does not

constitute an endorsement.

For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and

expressions related to conformity assessment, as well as information about ISO's adherence to the

World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www .iso

.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.
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ISO/IEC TR 23186:2018(E)
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.

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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.]
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ISO/IEC TR 23186:2018(E)
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

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.

DataKind® held a DATADIVE® to bring data scientists together to transform the available data and

develop the model.

One of the key challenges in this scenario is getting data owners to entrust their data to a group and to

a third-party processor. Specific concerns include:

— How are applicable regulations, policies and other data use restrictions identified and adhered to?

— How are privacy infringements avoided?

1) DataKind is the service mark of DataKind. This information is given for the convenience of users of this document

and does not constitute an endorsement by ISO or IEC.

2) DATADIVE is the service mark of a service supplied by DataKind. This information is given for the convenience

of users of this document and does not constitute an endorsement by ISO or IEC of the service named. Equivalent

services may be used if they can be shown to lead to the same results.
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ISO/IEC TR 23186:2018(E)
— What processes are employed to provide end-to-end security?
— How is data provenance maintained?
— What is the proof of compliance?

Figure 1 illustrates the system for predicting traffic accident patterns as described above.

Figure 1 — Example of a system for predicting traffic accident patterns using multi-sourced data

5.2 Using multi-sourced data for home automation

A variety of emerging home automation applications could benefit from access to a larger variety of

data, potentially sourced from multiple providers and processed in a coordinated and timely manner.

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ISO/IEC TR 23186:2018(E)

The general goal for any home application is to use available data to improve the efficiency and

effectiveness of the home both as a part of a municipality and as a desirable place to live. To do this,

home IoT/IS systems need to be informed, agile and more dynamic for its residents, both for managing

the building itself and also for the quality of life inside the home.

There are many home services being developed by many service providers and manufacturers, ranging

from smart entertainment systems (TV, Internet, telephone, wall art display, etc.) to emergency

detection and alarms to smart electricity and water management.

Some examples of multi-sourced data for home operations processing could include:

— Time signals from public sources;
— Weather forecast information and current state for the home area;

— Neighbourhood information, e.g. alerts, fire alarms, air quality, road congestion;

— Sensor-based data from multiple multivendor systems located within the house, e.g. locks,

temperature, appliance state and status, e.g. refrigerator breakdowns, occupancy status (is anyone

home?), connectivity status, electricity and water status and meter readings;
— Home service maintenance, support and billing information;
— Visual and audio data sources (internal and external);
— Health emergency and intrusion alarms;

— Calendar and current location information for residents, i.e. who is expected to be home and when;

— Policies and configuration settings, e.g. water rates, electricity costs, time-of-day rules.

Information sources could be classified as:

— Public information, e.g. public databases, governments, municipalities, legal rules, supply rates;

— Home-based information, e.g. IoT sensors in and around the home. These could be distinct sources,

e.g. from different vendors equipment, or could be aggregated and delivered from a hub; this could

include both real-time data, human user input data, and archival data;

— Related element sources, e.g. occupant vehicle data, manufacturer information, opportunity

information, e.g. local events, component replacement sales;
— Historical insights and trends;
— Policies and rule settings from governments, vendors and residents.

All data would be accessed, combined, processed and managed both independently and in combination

to provide an increasingly intelligent basis for home activity automation and home operations control

and protection. Trust is needed to avoid accidents, spoilage, inefficiencies and false actions within and

around the home.

Many point solutions using independent data sources could benefit from reliable processing in a more

coordinated and orchestrated way. For example, if the home temperature control system receives input

of the weather forecast, the home inside temperature and arrival time of any occupants, the energy

balance could be more efficiently optimized.
5.3 Using multi-sourced data for automotive operations

The term “car” represents a wide range of vehicle types that may have very similar requirements. The

manufacturers, owners, drivers and occupants of a car may be customers of the car’s cloud-based and

on-board systems.
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Car applications can benefit from access to data from multiple sources that is made available in a

coordinated, timely and trusted manner. Two use cases are the collection of information for use:

— by the car itself (an on-board “cloudlet”) or its cloud-based proxy for driving purposes; or

— by insurance companies, car manufacturers, cities, governments, and others for related and off-line

services such as maintenance, usage tracking, congestion management, and many other possibilities.

General goals for any car are to optimize the user (passenger) experience, reduce transportation costs

and delays, improve safety, and maximize vehicle life. To do this, car automation systems need to be

informed, agile and dynamic especially if self-driving or assisted-driving systems are being used.

Car-related services range from in-car social networking to route management to emergency alarms and

collision avoidance. In addition, considerable information may be collected for repair and maintenance

or for defect detection. Other applications try to make the car a “home away from home” and could

provide access to all the data that would be available at home.

A car could be viewed as a cluster of “IoT things,” each of which may require feeds from different data

sources or may interact with external systems that process data from many sources. There may be

hundreds of sensors associated with a single car.
Examples of multi-sourced data for car operation could include:
— Time signals from public sources;
— Weather information and current state for areas of interest;

— Road and surroundings information, e.g. blockages, congestion, accidents, disasters, which could

come from many sources including other cars;

— Sensor-based data from within the car, e.g. locks, internal/external temperature, component (e.g.

engine) state and status, driver and occupant status, which can be used directly by the “car cloud”

or used remotely with results fed back to the car;

— Car maintenance, support and service information (both collected and reported);

— Visual, audio and data sources (internal and external) for passenger use;
— Occupant health status, emergency and intrusion (break-in) alarms;
— Information from other ecosystems of interest, e.g. home, office.

All these data sources could be accessed, combined, processed and managed both independently and

in combination to provide an increasingly intelligent basis for car operations, control and protection.

From the macro perspective, information from many cars can be collected and processed to develop

information for the car suppliers, city planners and regulatory agencies.

Many point solutions using independent data sources could usefully be coordinated and shared. For

example, if a car knows the weather forecast, the current road conditions and the amount of fuel

available, then road selection and rest/re-fuelling stops could be more effectively orchestrated and

optimized.
6 Trust

Trust is a key element in the processing of multi-sourced data. Trust has a variety of meanings and

forms for the various parties associated with the data and processing of the data depending on different

perspectives. The parties involved include the organization(s) processing the data, the organization(s)

which are the sources of the data, people whose PII is contained within any of the data, and finally

people and/or organizations who use the output of the processing.
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For an organization processing the data, one of the major elements of trust concerns the provenance of

the data that they use: how was the data put together, how reliable is the information it contains, does

the data require cleansing or filtering, how complete is the data it contains, does the data contain PII or

confidential information of any kind. Other issues concern any regulations and laws that might apply

to the data and any commercial terms that apply to the data that might affect the planned processing.

For an organization as the source of data, the major element of trust concerns whether the processing

organization uses data as authorized. The essential questions may include:

— Does the processing organization make a clear statement about the intended uses?

— Does the processing organization sign an agreement in relation to this processing, and particularly

agree to abide by any restrictions or regulations that apply to the data (both regarding commercial

terms, if any, and any regulations or laws that apply)?

— Does the processing organization have appropriate certifications or equivalent proofs in relation to

the processing, including appropriate security controls and PII protection?

For any individuals who have PII contained in any of the data used for processing, the major concern

is that the PII is processed transparently and only for purposes that have been clearly stated to the

individuals and for which consent has been obtained. A major concern relating to any PII breaches that

might occur is whether all necessary measures are in place to prevent such breaches.

Finally, for the people or organizations using the output of the processing, the key element of trust

concerns their ability to rely on that output, that it is correct, that it is unbiased, that it matches any

claims made for the output by the processor.
7 Data access and processing rights

Establishing who can use, process and pass on data, and understanding the rights various parties have

over the data is essential for a multi-sourced data ecosystem.

From the discussion of ownership of data, the example of a car ecosystem is a quintessential use case in

establishing data access and the rights to view, modify, copy, or process the data considering there could

be distinct sources of data from the various components, the component vendor, the car manufacturer,

or the owner of the car, who can own the data after it is generated. Technically, an identity and access

management (IdAM) system provides the tools to control who can access a resource and when, and

thereby the IdAM system owner grants access to different parties.

It could be argued that the traditional legal notion of ownership does not fit with this new model, and

either new constructs are required, or the notion of co-ownership needs to be further explored. This

discussion on ownership is further complicated when discussing data aggregated from multiple sources

which is then processed through AI or machine learning systems, for example.

Regardless of who owns the data, there could be legal rights for parties to access, process and collect

data. For example, to ascertain the cause of a collision, police might have an automatic entitlement to

all the data in a car after a collision with fatalities. Another example, related to PII, where the owner

or driver of the vehicle could be entitled to copies of any data that has been collected or extracted.

In Europe the General Data Protection Regulation (GDPR) and revisions to the Directive on Privacy

and Electronic Communication (Directive 2002/58/EC) describe the legal basis for different types of

processing of data. Another scenario is that local laws could require that the results of processed data

be made publicly available, suitably anonymized, if the system has been publicly funded, e.g. real time

traffic data or data relating to public transport services.
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8 Framework for trusted processing of multi-sourced data
8.1 Introduction
Figure 2 — Framework for trusted cloud processing of multi-sourced data

Figure 2 depicts “elements of trust” and a representative data flow for the processing of multi-sourced

data. Descriptions for each part of Figure 2 are provided later in this document.

Standards to support the elements of trust will likely come from multiple JTC 1 and ISO committees

such as JTC 1/SC 27, JTC 1/SC 32, JTC 1/SC 40, JTC 1/SC 41, ISO TC 307 and others. The elements of trust

will likely also be informed by sector specific regulations and societal norms.
8.2 Data flow

The representative data flow shown in Figure 2 is provided only to establish a context for the elements

of trust and should not be construed as the only way to architect processing of multi-sourced data.

However, the various examples described in this document such as multi-sourced data for car operation

have shown that it cannot be a simple direct flow where one system ends, and another begins. For a

cloud computing reference architecture, see ISO/IEC 17789. For additional information on cloud

computing data flows see ISO/IEC 19944. For IoT dataflows see ISO/IEC 30141.

Data can come from any number of sources, including data brokers , and can include both proprietary

and open data. The data can also have different formats, schema and security schemes along with

different restrictions on its use. Multi-sourced data can be brought in over a network or by other means

such as portable data storage appliances. The data may need to be wrangled and cleansed before it is

suitable for processing.

There is industry uptake for putting computing resources at the edge of the network for applications

that require real time data processing when associated with devices from cars and medical devices

to consumer products and industrial machinery. Data is processed and analysed by placing servers or

gateways in the proximity of the devices. This can be done to reduce latency when transporting data to

a cloud service provider or data centre. The elements of trust described in this document are important

to these scenarios as well.

3) Data brokers collect data from one or more sources and provide the data to one or more customers.

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Several cloud data storage types are suitable for multi-sourced data. Multiple da

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