DATA PRIVACY, ETHICS AND PROTECTION
GUIDANCE NOTE ON BIG DATA FOR
ACHIEVEMENT OF THE 2030 AGENDA
This document has been approved through the United
Nations Development Group (UNDG) and applies to all
member entities within the UNDG and its working mechanisms.
The approval of this document is based on consensus among
UNDG members and the provisions contained herein apply
to all UNDG entities; FAO, IFAD, ILO, IOM, ITU, OHCHR,
UNAIDS, UNCTAD, UNDESA, UNDP, UNECA, UNECE, UNECLAC,
UNEP, UNESCAP, UNESCO, UNESCWA, UNICEF, UNIDO,
UNFPA, UNHABITAT, UNHCR, UNODC, UN OHRLLS, UNOPS,
UN OSAA, SRSG/CAC, UN Women, UNWTO, WFP, WHO and
WMO.
PURPOSE OF THIS GUIDANCE NOTE 2
PRINCIPLES 4
1. LAWFUL, LEGITIMATE AND FAIR USE 4
2. PURPOSE SPECIFICATION, USE LIMITATION AND PURPOSE COMPATIBILITY 4
3. RISK MITIGATION AND RISKS, HARMS AND BENEFITS ASSESSMENT 4
4. SENSITIVE DATA AND SENSITIVE CONTEXTS 5
5. DATA SECURITY 5
6. DATA RETENTION AND DATA MINIMIZATION 6
7. DATA QUALITY 6
8. OPEN DATA, TRANSPARENCY AND ACCOUNTABILITY 7
9. DUE DILIGENCE FOR THIRD PARTY COLLABORATORS 7
DEFINITIONS & NOTES 8
ADDENDUM A 12
HOW DATA ANALYTICS CAN SUPPORT THE SDGs 12
BIBLIOGRAPHY 14
TABLE OF
CONTENTS
1 TABLE OF CONTENTS
This document sets out general guidance on data privacy,
data protection and data ethics for the United Nations
Development Group (UNDG) concerning the use of big
data, collected in real time by private sector entities as
part of their business oerings
1
, and shared with UNDG
members for the purposes of strengthening operational
implementation of their programmes to support the
achievement of the 2030 Agenda
2
. The Guidance Note
is designed to:
● Establish common principles across UNDG to support
the operational use of big data for achievement of the
Sustainable Development Goals (SDGs);
● Serve as a risk-management tool taking into account
fundamental human rights; and
● Set principles for obtaining, retention, use and quality
control for data from the private sector.
The data revolution was recognized as an enabler of the
Sustainable Development Goals, not only to monitor progress
but also to inclusively engage stakeholders at all levels to
advance evidence-based policies and programmes and to
reach the most vulnerable.
3
The 2030 Agenda asserts that
“Quality, accessible, timely and reliable disaggregates data will
be needed to help with the measurement of progress (SGDs)
and to ensure that no one is left behind. Such data is key to
decision making.”
4
PURPOSE OF
THIS GUIDANCE NOTE
At the same time, there are legitimate concerns regarding
risks associated with handling and processing of big data,
particularly in light of the current fragmented regulatory
landscape and in the absence of a common set of principles
on data privacy, ethics and protection. These concerns
continue to complicate eorts to develop standardized
and scalable approaches to risk management and data
access. A coordinated approach is required to ensure the
emergence of frameworks for safe and responsible use of
big data for the achievement of the 2030 Agenda.
The guidance described in this document acknowledges
and is based on the UN Guidelines for the Regulation
of Computerized Personal Data Files, adopted by the
UN General Assembly resolution 45/95, and takes into
account both existing international instruments and
relevant regulations, rules and policies of UNDG member
organizations concerning data privacy and data protection.
This Guidance Note is based on standards that have
withstood the test of time, reflecting the strength of their
core values.
This Guidance Note is designed to support members and
partners of the UNDG in establishing ecient and coherent
data collaborations.
1 This Guidance Note focuses on the use of big data collected by non-UN parties.
Many of the guiding principles, however, may be applied in cases where UNDG
members collect big data for the purposes of implementing their mandates, for
example in the form of photographs or videos collected by unmanned aerial vehi-
cles (UAVs).
2 This Guidance Note supports implementation of the Quadrennial Comprehensive
Policy Review of the operational activities for development of the UN system
(A/C.2/71/L.37), particularly in its call for the UN funds, programmes and specialized
agencies to strengthen their support to “collect, analyse and increase significantly
the availability of high-quality, timely and reliable disaggregated data….and in so
doing utilizing national capacities to the fullest extent possible in the context of
United Nations operational activities for development.”
3 Examples of how big data could be used to support the Sustainable Development
Goals can be viewed in Addendum A.
4 For more detail, see Transforming our World: The 2030 Agenda for Sustainable
Development (A/RES/70/1, p. 11), available at https://sustainabledevelopment.un.org/
post2015/transformingourworld.
5 The right to privacy is enshrined by the Universal Declaration of Human Rights,
Article 12 (UN General Assembly resolution 217 A(III), Paris, France, 10 December
1948.; the International Covenant on Civil and Political Rights, Article 17 (General
Assembly resolution 2200 A(XXI), New York, 19 December 1966, UN Treaty Series,
vol. 999, No. 14668, p. 171 and vol. 1057, p. 4019); the Convention on the Rights
of the Child (art. 16), the International Convention on the Protection of All Migrant
Workers and Members of Their Families (art. 14); European Convention on Human
Rights (art. 8); the American Convention on Human Rights (art. 11).
2 DATA PRIVACY, ETHICS AND PROTECTION: GUIDANCE NOTE ON BIG DATA FOR ACHIEVEMENT OF THE 2030 AGENDA
Rearming that the right to privacy is a fundamental human
right and recognizing the social value of data, including the
value of disaggregated SDG indicators with regard to the
implementation of the 2030 Agenda, this document aims to
provide a harmonized general framework for accountable,
adequately transparent, and responsible data handling
practices across the UNDG and with partners.
This Guidance Note is not a legal document. It provides
only a minimum basis for self-regulation, and therefore
may be expanded and elaborated on by the implementing
organizations.
Acknowledging the potential risks and harms as well as the
benefits that can result from big data use, this document
goes beyond individual privacy and considers potential
eects on group(s) of individuals. Additionally, this guidance
takes into consideration standards of moral and ethical
conduct, and recognizes the importance of context when big
data is being used.
It is recommended that this Guidance Note be implemented
through more detailed operational guidelines that account
for the implementation of UNDG member organizations’
UNDG is grateful to UN Global Pulse for developing
this Guidance Note and acknowledges the
invaluable contributions of the Global Pulse Privacy
Advisory Group as well as other private and public
expert stakeholders. Any questions, comments or
recommendations regarding this Guidance Note
should be directed to [email protected].
6 Sustainable Development Goal 17 aims to “Strengthen the means of implementation
and revitalize the global partnership for sustainable development”. Target 17.18
notes the need for data disaggregation by income, gender, age, race, ethnicity,
migratory status, disability, geographic location and other characteristics relevant in
national contexts (A/70/L.1.).
7 For more information, see the “Report of the Special Rapporteur on the right to priva-
cy”, Joseph A. Cannataci, Annex II. A more in-depth look at Open Data and Big Data
(A/HRC/31/64, p. 24).
mandates as well as their existing regulations, rules and
policies concerning data privacy, data protection, data ethics
and data security. It is recommended that designated legal,
ethics, privacy and security experts be consulted, when
necessary, regarding the implementation of, and compliance
with, this Note. Implementing organizations are encouraged
to establish a monitoring mechanism for compliance and
implementation of this Note.
Given continuous advances in technology and data, the
international landscape concerning data privacy and data
protection (including through the relevant work of the UN)
may also change. Accordingly, this note is a living document
and may also evolve over time.
3 PURPOSE OF THIS GUIDANCE NOTE
1. LAWFUL, LEGITIMATE AND FAIR USE
Data access, analysis or other use must be consistent with the
United Nations Charter and in furtherance of the Sustainable
Development Goals.
Whether directly or through a contract with a third party data
provider, data should be obtained, collected, analysed or other-
wise used through lawful, legitimate and fair means. In particular,
data access (or collection, where applicable), analysis or other
use should be in compliance with applicable laws, including
data privacy and data protection laws, as well as the highest
standards of confidentiality and moral and ethical conduct.
Data should always be accessed, analysed or otherwise used
taking into account the legitimate interests of those individuals
whose data is being used. Specifically, to ensure that data use
is fair, data should not be used in a way that violates human
rights, or in any other ways that are likely to cause unjustified
or adverse eects on any individual(s) or group(s) of individuals.
It is recommended that legitimacy and fairness of data use is
always assessed taking into account risks, harms and benefits
as discussed in Section 6.
Big data often contains personal data and sensitive data. The
use of personal data should be based on one or more of the
following legitimate and fair bases, subject to implementing
UNDG member organizations’ regulations, rules and policies
(including data privacy and data protection policies): (i) adequate
consent of the individual whose data is used, (ii) in accordance
with law, (iii) furtherance of international organizational mandates,
(iv) other legitimate needs to protect the vital or best interest of
an individual(s) or group(s) of individuals.
2. PURPOSE SPECIFICATION, USE
LIMITATION AND PURPOSE COMPATIBILITY
Any data use must be compatible or otherwise relevant, and not
excessive in relation to the purposes for which it was obtained.
The purpose of data use cannot be changed unless there is a
legitimate basis, as noted in Section 1. The purpose should be
legitimate and as narrowly and precisely defined as practically
possible. Requests or proposals for data access should be
tailored to a specific purpose.
There must be a legitimate and fair basis for an incompatible
deviation from the purpose for which the data was obtained.
However, mere dierence in purpose does not make a new
purpose incompatible. In determining compatibility, for example
the following criteria could be considered: how deviation from
the original purpose may aect an individual(s) or group(s) of
individuals; or the type of data used (e.g. public, sensitive or
non-sensitive); or measure taken to safeguard the identity of
individuals whose data is used (e.g. pseudonymization,
masking, encryption).
The purpose of data access (or collection where applicable)
should be articulated no later than the time of data access (or
collection where applicable).
3. RISK MITIGATION AND RISKS, HARMS
AND BENEFITS ASSESSMENT
A risks, harms and benefits assessment that accounts for data
protection and data privacy as well as ethics of data use should
be conducted before a new or substantially changed use of
data (including its purpose) is undertaken. Appropriate risk
mitigation measures should be implemented. Individual(s) or
group(s) of individuals should not be exposed to harm, or to
undignified or discriminatory treatment, as a consequence of
data use by UNDG member organizations.
PRINCIPLES
4 DATA PRIVACY, ETHICS AND PROTECTION: GUIDANCE NOTE ON BIG DATA FOR ACHIEVEMENT OF THE 2030 AGENDA
Any risks, harms and benefits assessment should take into
consideration the context of data use, including social,
geographic, political and religious factors. Such assessments
should account for potential physical, emotional or economic
harms, as well as any harms that could be caused as a result of
infringement of individual(s)’ rights.
Any risks, harms and benefits assessment should take into con-
sideration the impact that data use may have on an individual(s)
and/or group(s) of individuals, whether legally visible or not and
whether known or unknown at the time of data use.
An assessment of harms should consider such key factors as: (i)
the likelihood of occurrence of harms, (ii) potential magnitude of
harms and (iii) potential severity of harms.
Additionally, the assessment should take into account the digital
literacy of both potential users of data and those individuals
whose data is being used.
Where possible, the assessment should be completed by a
diverse team of experts (e.g. legal, ethics and security experts
as well as subject-matter experts) and, where reasonably practi-
cal, a representative of the group(s) of individuals who could be
potentially aected.
The risk of harm is much higher for sensitive data, and stricter
measures for protection should apply if such data is explicit
personal data or is reasonably likely to identify an individual(s)
or a group(s) of individuals.
Decisions concerning the use of sensitive data should involve
consultation with groups concerned (or their representative)
where possible to mitigate any associated risks.
Additionally, it is important to take into account risks associated
with data breaches and vulnerable data security systems, as
noted in Section 3.
Use of data should be based on the principle of proportionality.
In particular, any potential risks and harms should not be
excessive in relation to the positive impacts (benefits) of data
use. Furthermore, assessing the eect of data on individual
rights in conjunction with each other is recommended wherever
possible, rather than taking rights in opposition to each other.
The risks, harms and benefits assessment can be a tool that
helps to assist with determination of whether the use of data is
legitimate, appropriate or fair.
4. SENSITIVE DATA AND
SENSITIVE CONTEXTS
Stricter standards of data protection should be employed while
obtaining, accessing, collecting, analysing or otherwise using
data on vulnerable populations and persons at risk, children and
young people, or any other sensitive data.
It is important to consider that context can turn non-sensitive
data into sensitive data. The context in which the data is used
(e.g. cultural, geographic, religious, the political circumstances,
etc.) may influence the eect of the data analysis on an individ-
ual(s) or group(s) of individuals, even if the data is not explicitly
personal or sensitive.
5. DATA SECURITY
Data security is crucial in ensuring data privacy and data pro-
tection. Taking into account available technology and cost of
implementation, robust technical and organizational safeguards
and procedures (including ecient monitoring of data access
and data breach notification procedures) should be implemented
to ensure proper data management throughout the data lifecycle
and prevent any unauthorized use, disclosure or breach of
personal data.
Proactively embedding the foundational principles of Privacy by
Design and employing privacy enhancing technologies during
every stage of the data life cycle is strongly recommended
as a measure to ensure robust data protection, in an eort to
prevent risks to privacy and harms from arising.
Personal data should be de-identified, where appropriate, using
such methods as aggregation, pseudonymization, or masking,
for example, to minimize any potential risks to privacy, and
taking into account the likely occurrence of any potential harms
associated with data use and non-use. Where appropriate,
UNDG member organizations should consider working with
data that has been de-identified by third party data providers
prior to its disclosure to the UNDG member organizations.
5 PRINCIPLES
Encrypt personal and sensitive data when transferred to or
from any network-connected server. No de-identified data
should knowingly and purposely be re-identified, unless there
is a legitimate, lawful and fair basis as noted in Section 1. To
minimize the possibility of re-identification, it is recommended
that de-identified data not be analysed or otherwise used by the
same individuals who originally de-identified the data.
It is important to ensure that the measures taken to protect
privacy and ensure data security do not disproportionately
compromise the utility of the data for the intended purpose.
Such measures should be employed in such a way as to
maximize the positive impact expected from the data use and
to fulfill the purposes for which the data was obtained.
Data access should be limited to authorized personnel, based
on the “need-to-know” principle. Personnel should undergo
regular and systematic data privacy and data security trainings.
Prior to data use, vulnerabilities of the security system
(including data storage, way of transfer, etc.) should be assessed.
Data security measures should be assessed in light of the risks,
harms and benefits of data use, including as noted in Section 3.
When considering the risks associated with the vulnerability of
data security systems, it is important to consider factors such
as intentional or unintentional unauthorized data leakage or
breach: (i) by authorized personnel, (ii) by known third parties
who have requested or may have access, or may be motivated to
get access to misuse the data and information, (iii) by unknown
third parties (e.g. resulting from publishing data sets or the
results of an analysis).
Special attention should be paid when using cloud services,
especially with regard to the data security setup and physical
locations at which data is stored. Usage of non-cloud storage
should be considered for sensitive data. When third-party cloud
storage providers are used, potential risks and harms associated
with the use of such cloud storage, as detailed in Section 3,
should be both taken into account.
6. DATA RETENTION AND DATA
MINIMIZATION
Data access, analysis or other use should be kept to the minimum
amount necessary to fulfill its purpose, as noted in Section 2.
The amount of data, including its granularity, should be limited
to the minimum necessary. Data use should be monitored to
ensure that it does not exceed the legitimate needs of its use.
Any retention of data
8
should have a legitimate and fair basis,
including beyond the purposes for which access to the data
was originally granted, as specified in Section 1, to ensure that
no extra or just-in-case data set is stored. Any data retention
should be also considered in light of the potential risks, harms
and benefits as discussed in Section 3. The data should be
permanently deleted upon conclusion of the time period
needed to fulfill its purpose, unless its extended retention is
justified as mentioned in this Section above. Any deletion of
data should be done in an appropriate manner taking into
consideration data sensitivity and available technology.
7. DATA QUALITY
All data-related activities should be designed, carried out,
reported and documented with an adequate level of quality
and transparency. More specifically, to the extent reasonably
possible, data should be validated for accuracy, relevancy,
suciency, integrity, completeness, usability, validity and
coherence, and be kept up to date.
Data quality should be carefully considered in light of the risks
that the use of low quality data for decision-making can create
for an individual(s) and group(s) of individuals.
Data quality must be assessed for biases to avoid any adverse
eects, where practically possible, including giving rise to
unlawful and arbitrary discrimination.
9 It is important to emphasize that big data generated by the use of social media,
mobile phones, credit cards, etc. is usually owned by either the original author or
the digital service provider (e.g. social media platform, mobile phone company or
bank).
10 Usually, there will be an opportunity to obtain consent if the organization is the
original data collector. However, in situations where data is being obtained from a
third party data provider, checking whether a third party data provider has obtained
adequate consent (e.g. directly or indirectly through the online terms of use) or has
another legitimate basis for collecting and sharing the data is recommended when
conducting a due diligence exercise.
6 DATA PRIVACY, ETHICS AND PROTECTION: GUIDANCE NOTE ON BIG DATA FOR ACHIEVEMENT OF THE 2030 AGENDA
Automatic processing of data, including the use of algorithms,
without human intervention and domain expertise should be
avoided when data is analysed for decision-making that is likely
to have any impact on an individual(s) or group(s) of individuals
to avoid potential harms resulting from low quality of data.
A periodic assessment of data quality is recommended during
the data life cycle. Furthermore, it is important to establish an
internal system of constant data updating and deletion of
obsolete data, where appropriate and practically possible.
8. OPEN DATA, TRANSPARENCY AND
ACCOUNTABILITY
Appropriate governance and accountability mechanisms should
be established to monitor compliance with relevant law, including
privacy laws and the highest standards of confidentiality, moral
and ethical conduct with regard to data use (including this
Guidance Note).
Transparency is a critical element of accountability. Being
transparent about data use (e.g. publishing data sets or
publishing an organization’s data use practices or the use of
algorithms) is generally encouraged when the benefits of
transparency are higher than the risks and possible harms.
Except in cases where there is a legitimate reason not to
do so, at minimum, the existence, nature, anticipated period
of retention and purpose of data use as well as the algorithms
used for processing data should be publicly disclosed and
described in a clear and non-technical language suitable for
a general audience.
Open data is an important driver of innovation, transparency
and accountability. Therefore, whenever possible, the data
should be made open, unless the risks of making the data open
outweigh the benefits or there are other legitimate bases not to
do so. Disclosure of personal information, even if derived from
public data, should be avoided or otherwise carefully assessed
for potential risks and harms as described in Section 3.
A risks, harms and benefits assessment (noted in Section 3)
should be one of the key accountability mechanisms for every
use of data, and should help determine what other governance
mechanisms may be needed to monitor compliance. In particular,
making data open or being transparent about data uses should
be considered a separate stage in the data life cycle, and thus it
is recommended that a separate assessment, noted in Section
3, is conducted to address risks, harms and benefits related to
that stage. The assessment should help determine the level of
openness and transparency.
9. DUE DILIGENCE FOR THIRD PARTY
COLLABORATORS
Third party collaborators engaging in data use should act in
compliance with relevant laws, including privacy laws as well as
the highest standards of confidentiality and moral and ethical
conduct. Their actions should be consistent with the United
Nations’ global mandate as well as UN regulations, rules and
policies. Furthermore, third party collaborators’ actions should
adhere to the Guidance Note, including to have a legitimate and
fair basis for sharing data with UNDG member organizations.
9
It is recommended that a process of due diligence be
conducted to evaluate the data practices of any potential third
party collaborators.
10
Legally binding agreements outlining parameters for data access
and handling (e.g. data security, data formats, data transmission,
fusion, analysis, validation, storage, retention, re-use, licensing,
etc.) should be established to ensure reliable and secure access
to data provided by third party collaborators.
7 PRINCIPLES
For the purposes of this document, data aggregation means
a process through which individual level data sets are
combined to such a format so they cannot be traced back
or linked to an individual. Typically, aggregated data is used
for analytical or statistical purposes to present a summary or
determine average results/numbers regarding age, gender,
community preferences, etc.
In addition, the UN Archives and Records Management
Section in its “Glossary of Recordkeeping Terms” defines
“aggregated records” as referring to “accumulated or collected
records that are organized into groupings or series”. From
the International Organization for Migration (IOM), the IOM
Data Protection Manual also defines “aggregate data” as
information, usually summary statistics, which may be compiled
from personal data, but are grouped in a manner to preclude
the identification of individual cases.
Consent is adequate, when it is freely given, explicit, informed
and in writing. Adequate consent should be obtained prior
to data collection or when the purpose of data re-use falls
outside of the purpose for which consent was originally
obtained.
To ensure that consent is informed, it is recommended that
as many details about the purpose of data use (e.g. any risks,
harms and potential positive and negative impacts) should
be included in the notice when the consent is sought. It is
important to note that in many instances consent may not be
adequately informed. Thus, it is important to consider assessing
the proportionality of risks, harms and benefits of data use
even if consent has been obtained.
Consent should be obtained before data is collected or
otherwise used, and individuals should have an opportunity
to withdraw their consent or object to the use of their data.
Checking whether a third party data provider has obtained
adequate consent (e.g. directly or indirectly through the
online terms of use) or has another legitimate basis for
sharing the data is recommended.
While there may be an opportunity to obtain consent at the
time of data collection, re-use of data often presents diculties
for obtaining consent (e.g. in emergencies where you may
no longer be in contact with the individuals concerned). In
situations where it is not possible or reasonably practical to
obtain informed consent, as a last resort, data experts may
AGGREGATION
OF DATA
ADEQUATE
CONSENT
DEFINITIONS & NOTES
8 DATA PRIVACY, ETHICS AND PROTECTION: GUIDANCE NOTE ON BIG DATA FOR ACHIEVEMENT OF THE 2030 AGENDA
still consider using such data for the best or vital interest of
an individual(s) or group(s) of individuals (e.g. to save their
life, reunite families, etc.). In such instances, any decision to
proceed without consent must be based on an additional
detailed assessment of risks, harms and benefits to justify
such action and must be found fair, lawful, legitimate and
in accordance with the principle of proportionality (e.g. any
potential risks and harms should not be excessive in relation
to the expected benefits of data use).
There are many definitions of big data. UN Global Pulse
in its report “Big data for development: Challenges and
opportunities” takes a traditional approach, defining big data
as “a massive volume of both structured and unstructured
data that is so large that it’s dicult to process with traditional
database and software techniques. The characteristics which
broadly distinguish big data are sometimes called the ‘3
V’s’: more volume, more variety and higher rates of velocity.”
The report provides examples of such data, including data
from sensors used to gather climate information, posts to
social media sites, digital pictures and videos posted online,
transaction records of online purchases, and from cell phone
GPS signals.
There are many types of big data with potential utility for
development. This document applies specifically to data
collected by the private sector in real time and that may
be used for the observation of human behaviour by UNDG,
thus aecting the decision-making process with regard to
the individual(s) or group(s) of individuals. Usually, such data
would be owned by an original author (e.g. a social media
user) or a digital service provider (e.g. a social media platform,
a
mobile phone company, a bank, etc.).
The International Telecommunication Union (ITU), in its
recommendation on “Big data – Cloud computing based
requirements and capabilities” defines big data as
BIG
DATA
DIGITAL
LITERACY
DEIDENTIFICATION
ANONYMIZATION
OF DATA
“a paradigm for enabling the collection, storage, management,
analysis and visualization, potentially under real-time constraints,
of extensive datasets with heterogeneous characteristics”.
For the purposes of this document, de-identification shall
mean a process of using all reasonable means to convert
personal data into anonymous data, such as that it cannot
be traced back or linked to an individual(s) or group(s) of
individuals. There are many dierent methods that could be
used to de-identify data. Examples include data aggregation,
masking, pseudonymization and k-anonymity.
De-identified data may be stripped of all personal identifiers
such as name, date of birth, exact location, etc.); however,
as noted in the UN Global Pulse Data Innovation Risk
Assessment Tool guidance, this data, although not directly or
explicitly identifying or singling out an individual or group(s)
of individuals, can still be linked to an individual(s) or
group(s) of individuals with the use of adequate technology,
skills and intent and thus may require the same level of
protection as explicit personal data.
For the purposes of this document, digital literacy means
how people understand the data they work with or share,
including how much they are aware of the positive and
negative impacts of data use and sharing. Digital literacy
concerns both actors who are using the data and those
whose data is being used.
9 DEFINITIONS & NOTES
In the glossary of the UN Archives and Records Management
Section, encryption is defined as a “security procedure that
translates electronic data in plain text into a cipher code by
means of either a code or a cryptographic system in order
to render it incomprehensible without the aid of the original
code or cryptographic system”.
For the purposes of this document, reference to a group(s) of
individuals also includes “legal” invisible (known or unknown)
group(s) of individuals, as adapted from the human-rights
based approach to data of the Oce of the United Nations
High Commissioner for Human Rights (OHCHR).
For the purposes of this document, masking means a
de-identification technique whereby the original personal
information collected from social media, such as comments,
photos and videos, is altered to such extent that it cannot
be traced back or linked to an individual(s) or group(s)
of individuals.
For the purposes of this document, personal data means data,
in any form or medium, relating to an identified or identifiable
individual who can be identified, directly or indirectly, by means
reasonably likely to be used, including where an individual can
be identified by linking the data to other information reasonably
available. Personal data is defined by many regional and
national instruments and can also be referenced as personal
information or personally identifiable information.
Personal data can be made private by its owner by restricting
access to it or made public by its owner (e.g. shared publicly
on social media). While sharing (and oversharing) personal
details about oneself and others on social networks has
become more common, such publicly available information
remains personal and it can pose risks to those individuals
represented in the data.
A report of the Special Rapporteur to the Human Rights
Council (A/HRC/23/40) defines privacy as “the presumption that
individuals should have an area of autonomous development,
interaction and liberty, a ‘private sphere’ with or without
interaction with others, free from State intervention and from
excessive unsolicited intervention by other individuals”. While
the majority of literature and legislature concentrates on “the
right to privacy”, in another such report (A/HRC/31/64), it has
been noted that there is currently no internationally accepted
definition of privacy.
ENCRYPTION PERSONAL
DATA
PRIVACY
GROUPS OF
INDIVIDUALS
MASKING
10 DATA PRIVACY, ETHICS AND PROTECTION: GUIDANCE NOTE ON BIG DATA FOR ACHIEVEMENT OF THE 2030 AGENDA
11 The approach was developed by Dr. Ann Cavoukian. A summary is available at
https://www.ipc.on.ca/wp-content/uploads/Resources/7foundationalprinciples.pdf
Privacy by Design
11
is an approach that promotes technology
design and engineering to incorporate privacy into the
design process from the start. The concept includes seven
guiding principles on privacy and security.
For the purposes of this document, pseudonymization
means modifying personal data by removing or substituting
all direct identifiers (e.g. in many instances name, address,
date of birth, etc.) with other unique identifiers (e.g. in many
instances hashing algorithms, ID numbers, etc.) in such a way
that it is still possible to distinguish a unique individual in a
data set. Masking is a type of pseudonymization.
For the purposes of this document, re-identification means a
process by which de-identified (anonymized) data becomes
re-identifiable again and thus can be traced back or linked
to an individual(s) or group(s) of individuals. As noted in
UN Global Pulse Data Innovation Risk Assessment Tool
guidance, to determine whether an individual(s) or group(s)
of individuals is identifiable, consider all of the means
reasonably likely to be used to single out an individual(s)
or group(s) of individuals. Factors to consider regarding
whether it is reasonably likely that an individual(s) or group(s) of
individuals can be re-identified include the required expertise,
costs and amount of time required for re-identification, and
reasonably and commercially available technology.
For the purposes of this document, sensitive data should be
considered as any data related to (i) racial or ethnic origin,
(ii) political opinions, (iii) trade union association, (iv) religious
beliefs or other beliefs of a similar nature, (v) physical or
mental health or condition (or any genetic data), (vi) sexual
orientation and other related activities, (vii) the commission
or alleged commission of any oence, (viii) any information
regarding judicial proceedings, (ix) any financial data, (x)
children and (xi) an individual(s) or group(s) of individuals that
face any risks of harm (e.g. physical, emotional, economic).
PRIVACY BY
DESIGN
PSEUDONYMIZATION
SENSITIVE DATA
REIDENTIFICATION
11 DEFINITIONS & NOTES
12 DATA PRIVACY, ETHICS AND PROTECTION: GUIDANCE NOTE ON BIG DATA FOR ACHIEVEMENT OF THE 2030 AGENDA
ADDENDUM A
HOW DATA ANALYTICS CAN SUPPORT THE SDGS
NO POVERTY
Spending patterns on
mobile phone services can
provide proxy indicators
of income levels
ZERO HUNGER
Crowdsourcing or tracking
of food prices listed online
can help monitor food
security in near real-time
GOOD HEALTH AND
WELL-BEING
Mapping the movement of
mobile phone users can
help predict the spread
of infectious diseases
QUALITY EDUCATION
Citizen reporting can
reveal reasons for
student drop-out rates
GENDER EQUALITY
Analysis of financial
transactions can reveal
the spending patterns
and different impacts
of economic shocks on
men and women
CLEAN WATER
AND SANITATION
Sensors connected to
water pumps can track
access to clean water
AFFORDABLE AND
CLEAN ENERGY
Smart metering allows
utility companies to
increase or restrict the
flow of electricity, gas
or water to reduce waste
and ensure adequate
supply at peak periods
DECENT WORK AND
ECONOMIC GROWTH
Patterns in global postal
traffic can provide indicators
such as economic growth,
remittances, trade and GDP
INDUSTRY,
INNOVATION AND
INFRASTRUCTURE
Data from GPS devices
can be used for traffic
control and to improve
public transport
REDUCED INEQUALITY
Speech-to-text analytics
on local radio content
can reveal discrimination
concerns and support
policy response
SUSTAINABLE CITIES
AND COMMUNITIES
Satellite remote sensing
can track encroachment
on public land or spaces
such as parks and forests
RESPONSIBLE
CONSUMPTION AND
PRODUCTION
Online search patterns or
e-commerce transactions
can reveal the pace
of transition to energy
efficient products
CLIMATE ACTION
Combining satellite imagery,
crowd-sourced witness
accounts and open data can
help track deforestation
LIFE BELOW WATER
Maritime vessel tracking
data can reveal illegal,
unregulated and unreported
fishing activities
LIFE ON LAND
Social media monitoring
can support disaster
management with
real-time information
on victim location,
effects and strength
of forest fires or haze
PEACE, JUSTICE
AND STRONG
INSTITUTIONS
Sentiment analysis of
social media can reveal
public opinion on effective
governance, public service
delivery or human rights
PARTNERSHIPS
FOR THE GOALS
Partnerships to enable the
combining of statistics,
mobile and internet data can
provide a better and real-
time understanding of today’s
hyper-connected world
BIG
DATA
SDGs
How data science
and analytics can
contribute to sustainable
development
www.unglobalpulse.org
@UNGlobalPulse 2017
NO POVERTY
Spending patterns on
mobile phone services can
provide proxy indicators
of income levels
ZERO HUNGER
Crowdsourcing or tracking
of food prices listed online
can help monitor food
security in near real-time
GOOD HEALTH AND
WELL-BEING
Mapping the movement of
mobile phone users can
help predict the spread
of infectious diseases
QUALITY EDUCATION
Citizen reporting can
reveal reasons for
student drop-out rates
GENDER EQUALITY
Analysis of financial
transactions can reveal
the spending patterns
and different impacts
of economic shocks on
men and women
CLEAN WATER
AND SANITATION
Sensors connected to
water pumps can track
access to clean water
AFFORDABLE AND
CLEAN ENERGY
Smart metering allows
utility companies to
increase or restrict the
flow of electricity, gas
or water to reduce waste
and ensure adequate
supply at peak periods
DECENT WORK AND
ECONOMIC GROWTH
Patterns in global postal
traffic can provide indicators
such as economic growth,
remittances, trade and GDP
INDUSTRY,
INNOVATION AND
INFRASTRUCTURE
Data from GPS devices
can be used for traffic
control and to improve
public transport
REDUCED INEQUALITY
Speech-to-text analytics
on local radio content
can reveal discrimination
concerns and support
policy response
SUSTAINABLE CITIES
AND COMMUNITIES
Satellite remote sensing
can track encroachment
on public land or spaces
such as parks and forests
RESPONSIBLE
CONSUMPTION AND
PRODUCTION
Online search patterns or
e-commerce transactions
can reveal the pace
of transition to energy
efficient products
CLIMATE ACTION
Combining satellite imagery,
crowd-sourced witness
accounts and open data can
help track deforestation
LIFE BELOW WATER
Maritime vessel tracking
data can reveal illegal,
unregulated and unreported
fishing activities
LIFE ON LAND
Social media monitoring
can support disaster
management with
real-time information
on victim location,
effects and strength
of forest fires or haze
PEACE, JUSTICE
AND STRONG
INSTITUTIONS
Sentiment analysis of
social media can reveal
public opinion on effective
governance, public service
delivery or human rights
PARTNERSHIPS
FOR THE GOALS
Partnerships to enable the
combining of statistics,
mobile and internet data can
provide a better and real-
time understanding of today’s
hyper-connected world
BIG
DATA
SDGs
How data science
and analytics can
contribute to sustainable
development
www.unglobalpulse.org
@UNGlobalPulse 2017
NO POVERTY
Spending patterns on
mobile phone services can
provide proxy indicators
of income levels
ZERO HUNGER
Crowdsourcing or tracking
of food prices listed online
can help monitor food
security in near real-time
GOOD HEALTH AND
WELL-BEING
Mapping the movement of
mobile phone users can
help predict the spread
of infectious diseases
QUALITY EDUCATION
Citizen reporting can
reveal reasons for
student drop-out rates
GENDER EQUALITY
Analysis of financial
transactions can reveal
the spending patterns
and different impacts
of economic shocks on
men and women
CLEAN WATER
AND SANITATION
Sensors connected to
water pumps can track
access to clean water
AFFORDABLE AND
CLEAN ENERGY
Smart metering allows
utility companies to
increase or restrict the
flow of electricity, gas
or water to reduce waste
and ensure adequate
supply at peak periods
DECENT WORK AND
ECONOMIC GROWTH
Patterns in global postal
traffic can provide indicators
such as economic growth,
remittances, trade and GDP
INDUSTRY,
INNOVATION AND
INFRASTRUCTURE
Data from GPS devices
can be used for traffic
control and to improve
public transport
REDUCED INEQUALITY
Speech-to-text analytics
on local radio content
can reveal discrimination
concerns and support
policy response
SUSTAINABLE CITIES
AND COMMUNITIES
Satellite remote sensing
can track encroachment
on public land or spaces
such as parks and forests
RESPONSIBLE
CONSUMPTION AND
PRODUCTION
Online search patterns or
e-commerce transactions
can reveal the pace
of transition to energy
efficient products
CLIMATE ACTION
Combining satellite imagery,
crowd-sourced witness
accounts and open data can
help track deforestation
LIFE BELOW WATER
Maritime vessel tracking
data can reveal illegal,
unregulated and unreported
fishing activities
LIFE ON LAND
Social media monitoring
can support disaster
management with
real-time information
on victim location,
effects and strength
of forest fires or haze
PEACE, JUSTICE
AND STRONG
INSTITUTIONS
Sentiment analysis of
social media can reveal
public opinion on effective
governance, public service
delivery or human rights
PARTNERSHIPS
FOR THE GOALS
Partnerships to enable the
combining of statistics,
mobile and internet data can
provide a better and real-
time understanding of today’s
hyper-connected world
BIG
DATA
SDGs
How data science
and analytics can
contribute to sustainable
development
www.unglobalpulse.org
@UNGlobalPulse 2017
NO POVERTY
Spending patterns on
mobile phone services can
provide proxy indicators
of income levels
ZERO HUNGER
Crowdsourcing or tracking
of food prices listed online
can help monitor food
security in near real-time
GOOD HEALTH AND
WELL-BEING
Mapping the movement of
mobile phone users can
help predict the spread
of infectious diseases
QUALITY EDUCATION
Citizen reporting can
reveal reasons for
student drop-out rates
GENDER EQUALITY
Analysis of financial
transactions can reveal
the spending patterns
and different impacts
of economic shocks on
men and women
CLEAN WATER
AND SANITATION
Sensors connected to
water pumps can track
access to clean water
AFFORDABLE AND
CLEAN ENERGY
Smart metering allows
utility companies to
increase or restrict the
flow of electricity, gas
or water to reduce waste
and ensure adequate
supply at peak periods
DECENT WORK AND
ECONOMIC GROWTH
Patterns in global postal
traffic can provide indicators
such as economic growth,
remittances, trade and GDP
INDUSTRY,
INNOVATION AND
INFRASTRUCTURE
Data from GPS devices
can be used for traffic
control and to improve
public transport
REDUCED INEQUALITY
Speech-to-text analytics
on local radio content
can reveal discrimination
concerns and support
policy response
SUSTAINABLE CITIES
AND COMMUNITIES
Satellite remote sensing
can track encroachment
on public land or spaces
such as parks and forests
RESPONSIBLE
CONSUMPTION AND
PRODUCTION
Online search patterns or
e-commerce transactions
can reveal the pace
of transition to energy
efficient products
CLIMATE ACTION
Combining satellite imagery,
crowd-sourced witness
accounts and open data can
help track deforestation
LIFE BELOW WATER
Maritime vessel tracking
data can reveal illegal,
unregulated and unreported
fishing activities
LIFE ON LAND
Social media monitoring
can support disaster
management with
real-time information
on victim location,
effects and strength
of forest fires or haze
PEACE, JUSTICE
AND STRONG
INSTITUTIONS
Sentiment analysis of
social media can reveal
public opinion on effective
governance, public service
delivery or human rights
PARTNERSHIPS
FOR THE GOALS
Partnerships to enable the
combining of statistics,
mobile and internet data can
provide a better and real-
time understanding of today’s
hyper-connected world
BIG
DATA
SDGs
How data science
and analytics can
contribute to sustainable
development
www.unglobalpulse.org
@UNGlobalPulse 2017
13 ADDENDUM A
14 DATA PRIVACY, ETHICS AND PROTECTION: GUIDANCE NOTE ON BIG DATA FOR ACHIEVEMENT OF THE 2030 AGENDA
Asia-Pacific Economic Cooperation
(2005) APEC Privacy Framework.
December. Singapore: APEC Secretariat.
Cavoukian, Ann (2011). Privacy by Design:
The 7 Foundational Principles. January.
Ontario, Canada: Information and Privacy
Commissioner of Ontario.
Council of Europe (1981). Convention
for the Protection of Individuals with
regard to Automatic Processing of
Personal Data. 28 January. ETS No. 108.
Strasbourg, Austria.
(1953). European Convention for
the Protection of Human Rights and
Fundamental Freedoms, as amended
by Protocols Nos. 11 and 14. 4 November.
ETS 5 (art 8).
Economic Community of West African
States (2010). Supplementary Act A/
SA.1/01/10 on Personal Data Protection
within ECOWAS. 16 February.
European Union (2016). Regulation (EU)
2016/679 of the European Parliament
and of the Council of 27 April 2016 on
the protection of natural persons with
regard to the processing of personal
data and on the free movement of such
data, and repealing Directive 95/46/
EC (General Data Protection Regulation)
(Text with EEA relevance).
International Committee of the Red
Cross (2015). ICRC Code of Conduct on
Data Protection. November. Geneva,
Switzerland: ICRC.
International Committee of the Red Cross
(2016). ICRC Rules on Personal Data
Protection. January. Geneva, Switzerland:
ICRC.
International Conference of Data
Protection and Privacy Commissioners,
Resolution on Data Protection and
International Organizations.
International Organization for Migration
(2010). IOM Data Protection Manual.
Geneva, Switzerland: IOM.
International Organization for
Standardization. Online Collection:
Information Security Management
Systems. Geneva, Switzerland: ISO.
International Telecommunication Union
(2015). Recommendation Y.3600. Big
data – Cloud computing based require-
ments and capabilities. 6 November.
Organisation for Economic Co-operation
and Development (1980). OECD
Guidelines on the Protection of Privacy
and Transborder Flows of Personal Data.
23 September. Paris: OECD.
BIBLIOGRAPHY
Organization of American States (1969).
American Convention on Human Rights,
“Pact of San Jose”, Costa Rica. 22
November. Washington, DC: OAS.
(art. 11).
United Nations (1989). Convention on the
Rights of the Child. Treaty Series 1577
(1989): 3 (art. 16).
United Nations Archives and Records
Management Section. Glossary of
Recordkeeping Terms.
United Nations Children’s Fund (2007).
The Paris Principles: Principles and
Guidelines on Children Associated
with Armed Forces or Armed Groups.
February. New York: UNICEF.
United Nations Data Revolution Group
(2014). A World that Counts: Mobilising
the data revolution for sustainable
development. The UN Secretary-General’s
Independent Expert Advisory Group
on a Data Revolution for Sustainable
Development. November.
United Nations Development Programme
and United Nations Global Pulse
(2016). A Guide to Data Innovation for
Development – From ideas to proof-of-
concept. New York: UN.
15 BIBLIOGRAPHY
United Nations Economic Commission
for Europe (2014). The Role of Big
Data in the Modernisation of Statistical
Production. Geneva, Switzerland: UNECE.
United Nations General Assembly (2016).
Quadrennial comprehensive policy
review of operational activities for devel-
opment of the United Nations system. 28
October. A/C.2/71/L.37.
A/70/L.1 (2015). Transforming Our World:
the 2030 Agenda for Sustainable
Development. 18 September.
A/RES/68/261 from 29 January 2014
(2014). Fundamental Principles of Ocial
Statistics. 3 March.
A/RES/45/158 (1990). International
Convention on the Protection of the
Rights of All Migrant Workers and
Members of their Families (art. 14). 18
December.
A/RES/45/95 (1990).Guidelines for the
regulation of computerized personal data
files. 14 December.
2200/A (XXI) (1966).International
Covenant on Civil and Political Rights. 19
December. UN Treaty Series, vol. 999,
No. 14668, p. 171 and vol. 1057, p. 4019
(art. 17).
217 A (III) (1948). The Universal
Declaration of Human Rights. 10
December. Paris, France. (art. 12).
Big data for development: Challenges
and opportunities, p. 13.
Principles. Data Privacy and Data
Protection Principles.
Privacy Tools. Risks, Harms, Benefits
Assessment.
United Nations High Commissioner for
Human Rights (2016). A Human Rights-
Based Approach to Data: Leaving No
One Behind in the 2030 Development
Agenda, Guidance Note to Data
Collection and Disaggregation. 19
February. Geneva, Switzerland; UNHCR.
(2015). Policy on the Protection of
Personal Data of Persons of Concern
to UNHCR. May. Geneva, Switzerland:
UNHCR.
United Nations Human Rights Committee
(1988). CCPR General Comment No 16:
Article 17 (Right to Privacy), The Right to
Respect of Privacy, Family, Home and
Correspondence, and Protection of
Honour and Reputation. 8 April.
United Nations Information Security
Special Interest Group (June 2013). Use
of Cloud Computing in the UN System,
Recommendations for Risk Mitigation.
United Nations International Law
Commission (2006). Report on the
work of the fifty-eight session (2006).
Annex IV. Protection of Personal Data in
Transborder Flow of Information.
United Nations Statistical Commission
(2015). Report of the Global Working
Group on Big Data for Ocial Statistics.
17 December. E/CN.3/2016/6.
United Nations, Human Rights Council
(2016). Report of the Special Rapporteur
on the right to privacy, Joseph A.
Cannataci. 8 March. A/HRC/31/64, pp.
6, 10. Annex II. A more in-depth look at
Open Data & Big Data.
Report of the Special Rapporteur on the
promotion and protection of the right to
freedom of opinion and expression, Frank
La Rue. 17 April. A/HRC/23/40, p. 6.
World Food Programme (2017). WFP
Guide to Personal Data Protection and
Privacy. 22 February. Rome: WFP.
World Health Organization (2016).
Guidance on Good Data and Record
Management Practices. WHO Technical
Report Series, No. 996. Annex 5.
@UNDGDOCO WWW.UNDG.ORG
The United Nations Development Group (UNDG) unites
the 32 UN funds, programmes, specialized agencies,
departments, and oces that play a role in development.
Since 2008, the UNDG has been one of the three pillars
of the UN System Chief Executives Board for Coordination,
the highest-level coordination forum of the United
Nations system.
At the regional level, six Regional UNDG Teams play
a critical role in driving UNDG priorities by supporting
UN Country Teams with strategic priority setting,
analysis and advice.
At the country level, 131 UN Country Teams serving
165 countries and territories work together to increase
the synergies and joint impact of the UN system.
The Development Operations Coordination Oce (DOCO)
is the secretariat of the UNDG, bringing together the
UN development system to promote change and innovation
to deliver together on sustainable development.
