Its column is termed an "attribute." Address, birth, and gender are examples of attributes. (...) (iii) Sensitive attribute: A significant attribute for secondary use is termed a "sensitive attribute," which can be selected from attributes that are not identifiers. The method will exclude this attribute from masking or generalization by anonymization.
Language:English
Score: 1112130
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https://www.itu.int/en/publica.../files/basic-html/page740.html
Data Source: un
Even risk factors arise beyond an individual’s control, e.g., when the amount of data held on an individual is in health insurance. kept to a minimum, their identity may be uncov- Big data may engage in differential pricing by ered through reverse-engineering from even a small drawing inferences from personal data about an indi- number of data points, risking violation of their vidual’s need for the service, and his or her capacity privacy. (...) For instance, if an algo- rithm sets higher prices for consumers with a post- • Directly identifying data identifies a person with- code from a neighbourhood that has historically had out additional information or by linking to infor- higher levels of default than those from other neigh- mation in the public domain (e.g., a person’s name, bourhoods, individuals who do not themselves have telephone number, email address, photograph, other attributes to suggest a higher risk may face social security number, or biometric identifiers). higher prices. • Indirectly identifying data includes attributes that Certain historically disadvantaged population can be used to identify a person, such as age, groups share particular attributes (such as a post- location and unique personal characteristics. code). Individuals with those attributes may there- by suffer discrimination even if they do not have a Big data, machine learning, consumer protection and privacy 29
26 27 28 29 30 31 32 33 34 35 36
Language:English
Score: 1110451
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https://www.itu.int/en/publica...t/files/basic-html/page31.html
Data Source: un
. • Citizen data protection and privacy measures are becoming increasingly common – so DFS operators should build them in even if the legislation is not yet in place, and ensure that any parties they provide with identity and attribute data (relying parties) take the same approach. • To this end, DFS operators should adopt and apply globally accepted “Privacy by Design” principles when dealing with and sharing personal data. 9 Glossary Term Range of meanings Identity • An individual, distinguishable from other individuals within a population. • The core attributes associated with an individual (name, address, date of birth). Attribute • A specific data item pertaining to an individual. Credential • An authentication token (e.g. smart card) used to assert identity. • A verifiable attribute, e.g. a digital certificate that demonstrates an entitlement or qualification.
Language:English
Score: 1105537.4
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https://www.itu.int/en/publica...n/files/basic-html/page65.html
Data Source: un
How wrongful gender stereotyping occurs in the Judiciary
Gender stereotypes
▪ ‘A gender stereotype is a generalized view or
preconception about attributes or
characteristics that are or ought to be
possessed by, or the roles that are or should be
performed by, men and women’
➢It’s a BELIEF
Gender stereotyping
▪ In contrast ‘gender stereotyping refers to the
practice of ascribing to an individual woman or
man specific attributes, characteristics, or
roles by reason only of her or his membership in
the social group of women or men’
➢It’s a PRACTICE
Gender stereotypes and gender
stereotyping
STEREOTYPE
(belief)
STEREOTYPING
(practice)
Generalized view or preconception about sex or gender
Assumptions about attributes, characteristics & roles of women/men
Inferences about
individual women and men
Gender stereotyping is pervasive in
society
▪ Women should not be stereotyped as a
homogeneous group
▪ Gender stereotyping can also undermine other
forms of gender identity
▪ Beyond gender stereotypes, there are many other
stereotypes related to age, ethnicity, disability
(intersectionality).
(...) Wrongful gender stereotyping
▪ The practice of ascribing to an individual woman or
man specific attributes, characteristics or roles, which
results in violation of human rights and fundamental
freedoms.
(...) Gender stereotyping in the Judiciary
▪ The term judicial stereotyping is to refer to the
practice of judges ascribing to an individual
specific attributes, characteristics or roles by
reason only of her or his membership in a
particular social group (e.g. women).
▪ It is used, also, to refer to the practice of judges
perpetuating harmful stereotypes by not
challenging stereotyping, for example by lower
courts or parties to legal proceedings.
Language:English
Score: 1086859.7
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https://www.ohchr.org/sites/de...icialStereotyping_Session3.pdf
Data Source: un
One approach to address machine learning’s poten- Techniques for removing bias based on a protect- tial tendency towards discrimination is to incor- ed attribute focus on ensuring that an individual’s porate randomness into the data. For instance, 138 predicted label is independent of their protected a machine learning algorithm for extending credit attributes. However, even if protected attributes may be trained using initial data that indicates that a 131 are not explicitly included, correlated attributes certain group (e.g., from a particular postcode or of a (proxies) may be included in the data set, resulting particular gender or race) tends to have less reliable in outcomes that may be discriminatory. (...) Busi- concerns may arise if discriminatory selection has nesses may also focus more on rapid growth to win adverse results for an individual. 137 the new market, while viewing discriminatory impact on protected groups as a lower level priority.
Language:English
Score: 1073759.1
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https://www.itu.int/en/publica...t/files/basic-html/page29.html
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Partial assertion For government and financial services, the set of identity attributes that need to be established and asserted is usually fixed, including, for example, name, address, and date of birth. There are many services where such a fixed set of attributes is not required. For example, access to age-restricted services may only require determining that the individual is over 18, and personalisation of a retail service may only strictly require information about product preferences (although often retailers are keen to acquire significantly more data). (...) More generally, with the increasingly diverse range of digital services that individuals use, there is a growing need for individuals (and the devices they own) to be able to share specific items of data within differing levels of assurance requirements, relevant to the context and shared under their control. 2.2 Derived digital identities Iteration of the process outlined in Figure 2 can be performed to derive different classifications of digital identity.
Language:English
Score: 1067362.9
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https://www.itu.int/en/publica...n/files/basic-html/page47.html
Data Source: un
Also, in Annex C to the Bar-Table Motion, a new telephone
number has been attributed to an alleged user while no allegations of communication
is made for that particular individual in Annex A of the Bar-Table Motion. (...) Annex C alleges that certain information demonstrate that certain phone numbers are
attributable to certain individuals. 11 ANNEX I1 (ICC-01/14-01/18-282-Conf-AnxI1) to the DCC and ANNEX D to the PTB (ICC-01/14-
01/18-723-Conf-AnxD) provide telephone number attributions for the individuals mentioned in
relation to the CDR in the DCC and in the PTB as well as the evidence supporting these attributions.
(...) REJECT in limine the new attributions and alleged communications involving the
individuals described in paragraph 8 above;
c.
Language:English
Score: 1066650.3
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https://www.icc-cpi.int/sites/.../CourtRecords/CR2022_03578.PDF
Data Source: un
1
FAO/WHO Joint Expert Meeting on Microbial Risk Assessment (JEMRA)
CALL FOR EXPERTS AND DATA ON MICROBIOLOGICAL RISK ASSESSMENT OF LISTERIA MONOCYTOGENES IN READY-TO-EAT (RTE) FOOD:
ATTRIBUTION, CHARACTERIZATION AND MONITORING
FAO and WHO are looking to identify experts to contribute to the future work of JEMRA in the area of Listeria monocytogenes in ready-to-eat foods.
In addition, FAO and WHO are requesting governments, the industry, academia, consumer groups, laboratories, and any other interested organizations and individuals to submit any available data and information on attribution, characterization and monitoring of Listeria monocytogenes in ready-to-eat foods.
(...) Foodborne outbreak and surveillance data related to Listeria monocytogenes
1) Foodborne outbreaks data:
• time of year and month in which the outbreak occurred • whether the outbreak / cases were confirmed or suspected regarding the link between the food
vehicle and the outbreak of human cases and how this was determined (e.g. laboratory confirmation, epidemiological investigation, etc.)
• number of cases, hospitalizations, and deaths associated with the outbreaks • age and sex distribution of cases (e.g. range and median) • individual host susceptibility characteristics of cases (e.g. pregnancy, nutrition, health and
medication status, concurrent infections, immune status and previous exposure history or any other risk factors identified)
• occurrence and number of secondary and tertiary transmission • the implicated food (if identified) and attributes of the food that may have been relevant in the
occurrence of the outbreak • level of Listeria monocytogenes in the food attributed • strains/serotypes of Listeria monocytogenes in the food attributed • origin (e.g. local, imported) of the food attributed • place of exposure • other information • relevant links (articles, reports, websites, etc)
2) Surveillance data on Listeria monocytogenes in foods:
• the implicated food and its attributes, if any • place of origin of food attributed
7
• level of Listeria monocytogenes in the food attributed • strains/serotypes of Listeria monocytogenes in the food attributed source and points of
exposure • other information • relevant links (articles, reports, websites, etc)
C.
Language:English
Score: 1062163.9
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https://www.fao.org/3/ca7352en/ca7352en.pdf
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Any existing services that facilitate the potential for individuals to produce a digital footprint can be leveraged for identity. Typically, platforms capable of establishing data attributes from alternative sources can be measured according to a two factor criteria: 1. Their ability to capture and structure useful information from traceable interactions between individuals and software. 2. Their ability to draw insight from the aggregated data they collect: including the relationship between individual data attributes and links between the attributes of separate entities.
Language:English
Score: 1059561.3
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https://www.itu.int/en/publica...n/files/basic-html/page52.html
Data Source: un
Using interests strongly correlated with protected attributes such as race can also be used to Another important challenge is the fact that the data exclude certain parts of the population to view, say, provided to advertisers comes from proprietary ads related to housing, employment or financial black boxes without an easy way for academics or services [29]. For our own research we never use others to audit aspects related to data quality. the “custom audiences” and only perform Whereas some user attributes such as age and secondary analysis of anonymous and aggregated gender are most likely derived from self-declared data. information, other attributes such as Facebook’s “Ex-pats (Germany)” are based on a proprietary 6. (...) At the same a Data2x “Big Data for Gender Challenge” grant. time, due to the possibility of (i) dynamically Details at http://data2x.org/big-data-challenge- targeting different sub-populations, and (ii) doing awards/#digital. so in a repeated manner, it cannot be ruled out that despite the aggregation and rounding of the REFERENCES returned audience estimates a sufficiently skilled attacker could abuse this data source and obtain [1] Barbara Adams, Karen Judd: The Ups and attributes for individual users. However, none of the Downs of Tiers: Measuring SDG Progress. data collected in any of the described or proposed Global Policy Watch, 2018. work contains individual level information and https://www.globalpolicywatch.org/blog/2 could not be used to obtain such information. 018/04/26/tiers-measuring-sdg-progress/ Apart from privacy concerns for individual users, there is the harder to address issue of group-level profiling.
Language:English
Score: 1057075.4
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https://www.itu.int/en/publica...2/files/basic-html/page53.html
Data Source: un
