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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
PRIVACY AND ONLINE SOCIAL NETWORK:
a model for analysis of collecting personal data
Privacidade e Rede Social Online: um modelo para análise de coleta de dados pessoais
Fernando de Assis Rodrigues (1), Ricardo Cesar Gonçalves Sant'Ana (2)
(1) Federal University of Pará (UFPA), Brazil, deassis@ufpa.br
(2) São Paulo State University (UNESP-Tupã), Brazil, ricardo.santana@unesp.br
Abstract
The popularization of Information and Communication Technologies allowed new forms of interaction,
including Online Social Network (OSN) services. These services could share personal data with third-party
organizations through Application Programming Interfaces (API), making it a complex task to observe the
data flow. This research proposes a model to identify levels and elements available in the flow of personal
data via API between organizations holding OSN services and external agents. It converged the recurrent
mechanisms into providing data sets from the OSN services to external agents from a systematized reading
of the technical documentation to establish a database model to fetch unitedly the possibility of storing
information about the levels in which API provided personal data to external agents, applying the Entity-
Relationship Model. Subsequently, it presents a Data Mart as a proof of concept for the proposed database
model, intending to compare the ways of accessing the personal data attributes stored and, following, shared
by the OSN services to external agents. It showed that the model could clarify the elements inherent in the
data flow, allowing a more structured analysis, including the possibility of monitoring changes in the API
by organizations over time.
Keywords: Data models; Database models; Privacy; Social networking
Resumo
A popularização das Tecnologias de Informação e Comunicação permitiu novas formas de interação,
incluindo os serviços de Rede Social Online (RSO). Esses serviços podem compartilhar dados pessoais
com organizações terceirizadas por meio de Interfaces de Programação de Aplicativos (API), tornando uma
tarefa complexa observar o fluxo de dados. Esta pesquisa propõe um modelo para identificar níveis e
elementos disponíveis no fluxo de dados pessoais via API entre organizações detentoras de serviços RSO
e agentes externos. Convergiu os mecanismos recorrentes em fornecer conjuntos de dados dos serviços
RSO para agentes externos, a partir de uma leitura sistematizada da documentação técnica para estabelecer
um modelo de banco de dados para buscar de forma conjunta a possibilidade de armazenar informações
sobre os níveis em que as API forneceram dados pessoais a agentes externos, aplicando o Modelo Entidade-
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
Relacionamento. Posteriormente, apresenta um Data Mart como prova de conceito para o modelo de banco
de dados proposto, pretendendo comparar as formas de acesso aos atributos de dados pessoais armazenados
e, em seguida, compartilhados pelos serviços RSO para agentes externos. Demonstrou que o modelo pode
esclarecer os elementos inerentes ao fluxo de dados, permitindo uma análise mais estruturada, incluindo a
possibilidade de monitoramento de mudanças na API pelas organizações ao longo do tempo.
Palavras-chave: Modelos de Dados; Modelos de Banco de Dados; Privacidade; Redes Sociais
Resumen
La popularización de las Tecnologías de la Información y la Comunicación permitió nuevas formas de
interacción, incluidos los servicios de Redes Sociales en Línea (RSO). Estos servicios pueden compartir
datos personales con organizaciones de terceros a través de interfaces de programación de aplicaciones
(API), lo que hace que sea una tarea compleja observar el flujo de datos. Esta investigación propone un
modelo para identificar niveles y elementos disponibles en el flujo de datos personales vía API entre
organizaciones que poseen servicios de RSO y agentes externos. Convergieron los mecanismos recurrentes
en la prestación de conjuntos de datos de servicios de RSO a agentes externos, a partir de una lectura
sistemática de la documentación técnica para establecer un modelo de base de datos para buscar en conjunto
la posibilidad de almacenar información sobre los niveles en los que las APIs proporcionaron datos
personales a agentes externos, aplicando el Modelo Entidad-Relación. Posteriormente, presenta un Data
Mart como prueba de concepto para el modelo de base de datos propuesto, con la intención de comparar
las formas de acceder a los atributos de los datos personales almacenados y luego compartidos por los
servicios de RSO a agentes externos. Demostró que el modelo puede aclarar los elementos inherentes al
flujo de datos, lo que permite un análisis más estructurado, incluida la posibilidad de monitorear los cambios
en la API por parte de las organizaciones a lo largo del tiempo.
Palabras clave: Modelos de datos; Modelos de bases de datos; Privacidad; Redes sociales
1 Introduction
The popularization of Information and Communication Technologies allowed new
interaction forms between individuals, communities, and public and private organizations, such as
those mediated by Online Social Network (OSN) services. These complex services are an integral
part of reflections on the new characteristics of society and the functioning of the social fabric,
with new possibilities of interaction and relationship between different types of entities, mediated
by applications, computers, and networks, as verified in the concepts of Cyberculture (Lévy 2001)
and Network Society (Castells 2009).
The OSN services are available globally (with few exceptions) as an integral part of a new
business model for organizations that usually offer these services free of charge to users but with
multiple considerations to enable profitability. Therein lies the duality and the antagonism of this
environment: the OSN services are free, accessible, with great potential to reduce geographic
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
barriers to interrelationships between individuals (Lengyel et al. 2015), but they are developed and
maintained as products that depend on profitability to ensure the viability of the business model of
the organizations who support them (Zhang et al. 2016).
The OSN organizations compete for public attention, bringing constant innovation to the
services offered, resulting from a highly competitive and profitable global market. A total of 4.2
billion people accessed an OSN service at least once in January 2021, representing 53.6% of the
population (Statista 2021), an increase of 490 million users between January 2020 and January
2021 (We Are Social 2021). It is also important to emphasize that the largest OSN holding
organizations are listed in the NASDAQ Top 100 Stock Index, except for some of the Chinese
organizations (NASDAQ 2021), in addition to being part of the most accessed applications and
websites (Alexa 2021).
Based on the news broadcast by the media, part of society understands that one of the forms
of profitability of organizations holding OSN services is related to the sale of advertising space for
their services (Wall Street Journal 2021). However, one of the main success factors that set OSN
services apart from other advertising spaces resides in the fact that they may be more assertive to
delimit the target audience expected by the advertiser since individuals exchange information
about their personal and professional activities in the OSN services, including natural and artificial
attributes, entertainment options, cultural options, among other details. In synthesis, individuals
tend to share personal data with the services (Rodrigues and Sant’Ana 2016).
These personal data are collected and stored in the Database Management Systems of the
organizations holding the OSN services. The collected data are strictly systematized, applying data
structure models and transforming them into data sets that, among other activities, allow a
systematic, continuous, uniform, and customizable recovery process for each type of informational
demand required (Rodrigues et al. 2018). Also, it is important to foreground the existence of
channels to data access for partners, called external agents (Rodrigues et al. 2018; Rodrigues
2017), representing a vital part of the revenue to OSN services (Lanier 2018). The OSN services
extend access to personal data to a set of organizations where the OSN service offers a dataset
supply channel (OSN service → External Agent) and determines which datasets will be available
to external agents without the requirement for human interaction in the process. The organizations
provide data through interfaces specifically for those exchange operations, widely adopting a
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
concept from Computer Science, developed in the 1960s, the Application Programming Interface
(API) (Manikas 2016). The APIs have become an integral component of the software that manages
OSN services and part of the model for building large software integrator solutions (Manikas
2016).
The OSN services became a complex research domain since the 1990s, analyzed from the
most varied perspectives and bringing new opportunities and challenges to researchers (Baatarjav
and Dantu 2011; Boyd and Ellison 2007). In this context, it is essential to build analysis' structures
that make it possible to clarify the elements of the data flow between OSN services and external
agents and to reduce the user's lack of awareness about shared personal data among these entities.
The motivating problem of this research is the difficulty in perceiving the levels at which the OSN
services deal with access to the personal data of their users and, consequently, support the
diagnoses of possible actions that potentiate breaches of the users' privacy from personal data that
is collected, stored and shared with external agents.
Therefore, this research proposes a model to identify levels and elements available in the
flow of personal data via API between organizations holding OSN services and external agents.
2 Literature Review
One of the leading research fields about OSN services use is related to the context of
potential data access actions that impact personal data privacy. The discussions on the subject of
personal data privacy are plural and addressed from different perspectives, such as the absence of
privacy guarantees in the data transferred between two nodes of the network, as in the sharing of
characteristics of the use of OSN services by men and women (Fogel and Nehmad 2009; Schneider
et al. 2009), by teenagers (Barnes 2006; Boyd 2013) and students (Acquisti and Gross 2006;
Ellison et al. 2007; Tufekci 2007); the lack (or the limited) of knowledge about how it works the
OSN services and its relationship with the privacy of personal data (Krasnova et al. 2009); self-
disclosure and publicity of personal or professional activities in OSN services (Trepte and
Reinecke 2011; Young and Quan-Haase 2009); the classification of activities with potential harm
to personal data's privacy in OSN services (Rodrigues and Sant’Ana 2016); the ethical use of
personal data from OSN services in scientific research (Zimmer 2010); the exposure and the
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
invasion of personal data stored in OSN services (Boyd 2008); and the leakage of personal data
(Krishnamurthy and Wills 2009).
However, the context for this research is related to scientific studies involving proposals of
analysis models that identify transmitted data on the network, besides the relationships between
content and actors, including service users, OSN services maintainers, and external agents. The
dynamics between content and actors integrated into the digital social fabric present complexity
for analysis of what happens to personal data, essentially in virtue of data and functionalities
constantly changing (Watts 2004). Stand out the research as the proposals for the analysis of
Uniform Resource Identifiers (URIs) used for the formation of the digital social fabric (users and
groups/communities) and the relationships between entities and contents transferred in the OSN
services (Mislove et al. 2007); the nucleation model to clustering users with common interests
through personal data (Zhang et al. 2017); the privacy aspects of personal data when shared with
OSN service partner platforms via third-party servers (Krishnamurthy and Wills 2008); the
analysis of user behavior and different uses of OSN services (Penni 2017); the data accessibility
model to elucidate the privacy and security risks and concerns in using OSN services (Creese et
al. 2012; Lankton et al. 2020); the content structuring in OSN services via the semantic web (Mika
2007; Rodrigues et al. 2018); and the detachment of OSN services spaces into user, social and
technological domains (Caviglione et al. 2014).
Hence, the leading global OSN services form the OSN supernetworks (Donath 2007),
overcoming the geographic barriers (Lengyel et al. 2015), bringing new marketing and
community-building possibilities (Zhang et al. 2016; Weber 2007), implying new ways of
obtaining social capital (Ellison et al. 2007), enabling new sentiment analysis techniques based on
the conveyed content (Khan et al. 2016; Pozzi et al. 2017), and personal data mining for behavior
analysis (Singla and Richardson 2008). Besides, OSN supernetworks bring new concerns, such as
security aspects of the information stored in these services (Altshuler 2013) and the development
of defense systems against web crawling in OSN interfaces (Mondal et al. 2011).
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
3 Material and Methods
The method adopted consists of an exploratory analysis of OSN services, by direct and
non-participant observation, of a quantitative and qualitative nature, with the use of combined and
convergent methods (Sandelowski 2000; Brannen 2005), from the exploration of the technical
characteristics of its APIs and the reading of document collections available. It was divided into
three perspectives, starting from a study of the steps available on OSN documentation (called
levels in this research) required to provide personal data from the OSN services to external agents,
followed by detailing the characteristics of personal attributes at the moment of data collection by
external agents, and a proposal of a database modeling to support query processes.
This research studied APIs from OSN services: Facebook, Twitter, and LinkedIn,
respectively, the Graph API (Meta, Inc. 2021), the Twitter API (Twitter, Inc. 2021), and the
LinkedIn API (Microsoft, Inc. 2021). Three factors established the eligibility criteria: the
availability of technical material in English, services free of charge to API users, and no access
limitation. Besides, those OSN services have the highest monthly access (Alexa 2021; Statista
2021). Were discarded to analyze other OSN services offered by the same organization, opting to
select only the service with the highest number of monthly users (e.g., Meta Inc. owns more than
one service).
It adopted a systematized reading of the technical documentation about the operation of
APIs made by OSN services and available to software developers. Afterward, it converged the
recurrent mechanisms to provide data sets from the OSN services to external agents. These two
steps are interrelated and described in the fourth section of this article. In the third stage, was
established a database model to fetch unitedly the possibility of storing information about the
levels at which APIs provided personal data to external agents, using the specialization →
generalization process and applying the Entity-Relationship Model (Silberschatz et al. 2011).
Subsequently, it presents a proof of concept for the proposed database model, intending to compare
the ways of accessing the personal data attributes stored and, following, shared by the OSN
services to external agents.
About the material, was used i) electronic spreadsheets to systematize the readings of
technical documents, including the collection of information about the components that are part of
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
the personal data collection process, in addition to the attributes of available personal data, ii)
Database Workbench tool for database modeling and script generation in Structured Query
Language (SQL) format, and, iii) MariaDB Database Management System to apply the script, to
enable the proof of concept of the model proposed in this research.
4 Provision of Data by OSN Services
The Facebook, LinkedIn, and Twitter APIs are similar in providing data to external agents,
except for the authorizing access process to the data for external agents. It is possible to assert that
Facebook subtly differs from the others by the degree of complexity, with more significant
numbers concerning the documentation available about features, object and attribute data, and in
the information about what could be done with the stored personal data on the terms of use.
Nevertheless, the issues discussed in this section are not based on the extension of the available
elements or the amount of existing content on these services, but on the levels of what is required
for the external agent to begin a process of collecting data in the OSN services.
Figure 1 – Flow diagram for OSN services data collection, via API, by external agents
Source: Authors.
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
As aforementioned, the API data collection process is subtly different in some technical
details. Facebook's Graph API differs from the others by adopting a two-level access authorization
system, while the other services analyzed use a one-level authorization system. However, for all
services analyzed, it is possible to synthesize the data collection process in 9 (nine) levels, detailed
in sequence in this section: API, Access Authorizations, Permissions, Requests, Parameters,
Qualifiers, Views, Attributes, and Relationships - all the levels are summarized in Figure 1. This
model for analysis of collecting data from these services discarded elements related to the terms
of use of the OSN services, which are part of the contract established between the organizations
holding the OSN services, external agents, and users.
The first level for collecting data is connecting an external application to the OSN service
via API. An interface as the API extends beyond a set of rules for data interoperability. The API
offers the possibility of connecting one or more information systems with the data source, with
platform independence, the feasibility of access control, and using standards and languages to
control the request and response of data sets, including documentation in the form of
operationalization.
Unlike other data collection methods, adopting an API allows greater independence for the
external agent to understand the possibility of implementing a continuous process of data inter-
operation between two information systems. This method does not require direct contact between
the external agent and the OSN service development teams to understand how requests could be
implemented on algorithms or how data will be retrieved when collecting. The OSN services may
have one or more APIs according to their size and market need. For example, Facebook designed
an API for advertising actions and business management.
The API functionalities are under constant development. It is common for OSN services to
use version control in implemented APIs. The OSN service designates an API version number
when adding or removing features. It is also relevant to highlight the use of existing technologies,
such as the Transmission Control Protocol/Internet Protocol (TCP/IP) for data transmission
between the origin and destination points, the HyperText Transfer Protocol Secure (HTTPS) as
the protocol to structure requests made by external agents, and the eXtensible Markup Language
(XML) or the JavaScript Object Notation (JSON) as markup languages applied to the data structure
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
transfer process. In some cases, it is also possible to identify datasets structured in Comma-
Separated Values (CSV).
The OSN services use Access Authorizations and Permissions to guarantee the feasibility
of different types of access to data sets in the same API. These services implement the Access
Authorizations in the form of tokens distributed by the organization holding the ONS service to
external agents, granting them access to a predetermined set of requests. Therefore, Access
Authorizations allow or deny requests for access to part of the data sets stored by OSN service,
which may include personal data. The tokens are the credentials that external agents must keep,
guard and use for the initial verification of credentials when requesting data to collect.
In the case of OSN service implementing authorization at one level, the Access
Authorization contains a static set of Permissions, defining possible executable requests by an
external agent. Yet, when an OSN service API applies a hierarchical access system, Access
Permissions may grant access to different Permissions. In other words, each Access Authorization
and Permission binomial could authorize a different number of requests, where OSN service users
must consent to access their data. For example, when a user accepts to share personal data with an
application external to the OSN service, the information system shows a dialog for users with
which sets of personal data will be susceptible to the external application to collect. In this
approach, the external agent will only be able to gather the personal data previously allowed by
the user.
Consequently, the Access Authorizations and Permissions grant multiple points of entry to
an API, each containing a set of Requests available for the external agent. The Requests are the
data collection entry points in the OSN services, with their individualized characteristics detailed
in technical documents made available by the OSN, aimed at the external agent's software
developers and, therefore, with a language-oriented towards this sort of professionals. Each
Request triggers a data access request from an expression in HyperText Transfer Protocol (HTTP)
REQUEST syntax via HTTPS. The Requisition response is transmitted to the external agent via
HTTPS and structured in XML and JSON markup languages or, to a lesser extent, in CSV format.
The Requests have a predefined set of Parameters to allow the external agent to customize
the data to be collected (e.g., filters and sort order). The technical documents of the Requests
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
detailed the Parameters, made available by the OSN, intended for software developers from the
external agent. The Parameters may also customize the Request response structure (e.g., change
the markup language from the Request's Response).
The Parameters could have Qualifiers, which are flags that identify the status of the
Parameter. The investigation identified a total of 3 (three) Qualifiers in the analyzed APIs: core
(the Parameter is part of the API’s core), default (the Parameter is a standard for API), and
deprecated (the Parameter discontinued in the version of the API).
The available data sets in each Request do not directly originate from the OSN service
database. The APIs implementation by these services uses information security's best practices
and, consequently, reduces the risk of allowing external agents to perform direct access to tables
from the OSN database that contains personal data. The API grant access to a View, a previously
selected set of attributes about an object stored in the database. For example, a User's Personal
Data Request does not allow access to the password attribute. As a result, each Request gives
access to a View containing a predetermined set of Attributes by the OSN.
Hence, each Request will supply access to a structured set of data from a View containing
a predetermined set of Attributes. It is possible to locate in the OSN services technical documents
the Attribute's data type (e.g., a number, a date, a string of characters, among others). Similar to
the Parameters, Attributes could also have Qualifiers: core (is part of the core of the API), default
(standard for API), and deprecated (deprecated in the API version).
Part of the View's Attributes has the function of linking to other Views available inside the
API. This function enriches the process, allowing the triggering of a Request to be the beginning
of a path to access and collect data sets from other Views, in which the external agent could access
data linked to the initial Request. This link between two Views through an Attribute is called a
Relation, an equivalent of Attributes marked with Integrity constraints (Primary Key - PK and
Foreign Key - FK) proposed in Entity-Relationship Model (Silberschatz et al. 2011). For example,
it is possible to access a user's data through a Relationship, collect data about their followers
(available in an Attribute on the first Request) and, next, collect data about each follower (a second
Request). The availability of Relationships is a crucial characteristic of analysis techniques such
as data drilling (The Apache Software Foundation 2020).
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
5 Model for Analysis of Collecting Personal Data
It has created a structure to store the information about the components and the levels of
data collection in OSN services, using the Entity-Relationship Model as a basis (Codd 1990;
Silberschatz et al. 2011). As data management, the MariaDB Database Management System –
open source – and the MySQL Workbench – compatible with MariaDB – were adopted to assist
in the model construction.
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a model for analysis of collecting personal data. Brazilian
Journal of Information Science: research trends, vol. 17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
Figure 2 – The Entity-Relationship Diagram about levels to provide personal data in OSN services
Source: Authors.
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
The model uses a total of 23 tables to represent this structure, interrelated (Figure 2), as
follows: eleven (11) main tables, representing nine (9) basic entities of the data collecting levels
in OSN services via API; two (2) tables to store information about OSN and their API versions;
five (5) auxiliary tables, for storing data that complement the minimum semantics of the system,
and; seven (7) tables to allow Many-to-Many (M:M) cardinality relationships.
Table 1 (in Appendix A) summarizes the model information about data tables and their
attributes, presenting the Data Dictionary containing the name and description of the informational
content of each table and its attributes, as well as the domain, size, and integrity constraints of each
table attribute. The model's main tables are osns, api, api_version, authorization_access,
permission, view, attribute, relation, request, parameter, and qualifier, systematized to store data
referring to the levels of the data collecting process in OSN services (Silberschatz et al. 2011). The
model's auxiliary tables are cardinality, data_format, data_type, data_value, and relation_type. The
authorization_access_attribute, authorization_access_permission_attribute,
authorization_access_permission_view, authorization_access_view, attribute_qualifier,
parameter_qualifier, and request_parameter are the associative entity tables, which are a set of
tables that assist the join of two or more entities in one-to-many and many-to-many modes
(Silberschatz et al. 2011). All tables adopted artificial primary keys composed of auto-incremental
integer numbers.
In function of the access to personal data by external agents through the OSN service, part
of the challenges in establishing an analysis perspective on aspects related to privacy endures in
the complexity of creating mechanisms that allow measuring information about the data set
accessible by APIs. The structure model to store the information proposed in this research
establishes this type of subsidy to analyze through different prisms. In other words, depending on
the objective or hypothesis raised, the researcher could build their search strategies to suffice their
demand (in the form of instructions in SQL Query format). When the data collected on the OSN
services are normalized and stored in a single structure, it allows a researcher to compare what is
recurrent or differs between the OSN services.
The benefit of structuring data in a model concomitant to the Entity-Relationship Model is
the possibility of creating other structures based on the original proposal. In this sense, it is possible
to reorganize the design of the initial structure to fit a specific demand. As a proof of concept, the
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
model was reorganized to a multidimensional form of a Data Mart with a star schema topology
(Kimball and Ross 2011; Inmon 2005; Silberschatz et al. 2011). The goal is to centralize the
perspective of the analysis to answer the required permission for access to each attribute.
As the aforementioned is a proof of concept, it decided to develop the new structure in the
MariaDB Database Management System, despite a specific tool for this type of analysis being
recommended (Thomsen 2002). However, the purpose is to simplify the analysis by researchers
outside areas related to the theory of multidimensional analysis. In this sense, the MariaDB
Database Management System offers essential support for the proposed activities.
Figure 3 – The Entity-Relationship Diagram of Data Mart structure with focus on data access permission
in OSN services
Source: Authors.
The Data Mart (Figure 3) has seven dimensions, each one representing an access point for
data query, all of which have the dimension_ prefix, as follows: osns, containing data about the
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RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
OSN services; api, containing data from each API; api_version, containing data for each API
version; authorization_access, including data about Access Authorizations; permission, with data
about the Permissions; view, having data about the Views, and; attribute, containing data about
the Attributes.
It is worth considering the discard of some structural dependencies, such as the relationship
between View and Attribute (view and attribute dimensions, respectively). It has chosen the star
schema instead of the snowflake schema to avoid complexity in the construct of SQL queries,
following the principle of de-normalization (Silberschatz et al. 2011).
The data of each dimension had origin in the first structure (Figure 2) processed from an
Extract, Transform, Load (ETL) task, an operation used to transform transactional datasets into
analytic and multidimensional datasets (Kimball and Ross 2011; Inmon 2005; Silberschatz et al.
2011). A dedicated Data Dictionary was dismissed for this model, as the reorganization into a
multidimensional form did not change the value of the data collected (Appendix A).
It was created a fact table in addition to the dimensions, called fact_access. The fact_access
table contains a set of foreign keys to relate to the dimensions and an attribute (labeled accessible)
to receive a Boolean value representing whether a given combination of dimension parameters
grants or denies access to OSN services data, via API, for external agents. A dataset from the
fact_access table is retrieved when someone triggers a query into one of the dimensions. The data
from the attribute accessible flagged with the boolean value TRUE will indicate which OSN
services data will be available for collection by external agents. Oppositely, the data flagged with
the boolean value FALSE will indicate which OSN services data will not be available for collection
by external agents, opening the possibility of developing eventual tautologies and contradictions.
For example, a researcher could perform a query A to retrieve if in a given OSN service
(S
x
), in an available API (A
x
), in an API version (AV
x
), with an Access Authorization (AA
x
) and
with a Permission (P
x
), in a View (V
x
), if an Attribute (Attr
x
) is available for access by an external
agent, represented by the Expression 1.
A = S
x
, A
x
, AV
x
, AA
x
, P
x
, V
x
, Attr
x
(1)
If a researcher requires to compare another Permission (Py) that has a different result from
query A, a new query B could be performed, represented by the Expression 2.
4
RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
B = S
x
, A
x
, AV
x
, AA
x
, P
y
, V
x
, Attr
x
(2)
Assuming that values retrieved from attribute accessible from queries A and B is unknown
and considering the TRUE value as synonymous with access granted (represented by the number
1) and FALSE value as synonymous with access denied (represented by the number 0). It is
possible to build a Truth Table (e.g., Table 2) to summarize all feasible queries, increasing the
potential for more complex expressions, for example, to treat queries as functions, part of the logic
proposed in Boolean Algebra.
Table 2 – Truth Table for queries A and B, including conjunction and disjunction operations
A
B
A ^ B (AND)
A v B (OR)
0
0
0
0
1
0
0
1
0
1
0
1
1
1
1
1
0 = False; 1 = True.
Source: Authors.
This logic behavior allows a researcher to use queries in SQL language tailored for each
type of analysis, for example, to use elements of Logic and Mathematics to quantify and qualify
the data access from these services by external agents.
5.1 Model Limitations
The proposed model has Minimum Viable Product (MVP) characteristics that require
adjustments to enable the comparison of data access via API with other techniques, such as
scraping data available in human-interaction interfaces, i.e., web scrapping User Interfaces (UI) in
HyperText Markup Language (HTML). It is common sense that there are non-expected ways of
collecting data in the UI through algorithms and adaptive libraries such as Python and Selenium,
respectively.
In addition, a relevant by-product of the model is the perception of datasets accessible to
external agents concerning information on OSN's Terms of Use. It was not practicable to confirm
whether this relationship could be operated directly in the proposed database model. There would
5
RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
be a requirement to transform unstructured data into structured data since the Terms of Use
documents do not currently have versions in machine-readable formats.
Another factor still under analysis is the possibility of inserting new semantic elements in
the model, allowing an improvement in understanding the impact on users on the data access to a
particular set of attributes for an external agent means. Besides, adding more semantics to the
model could identify elements of the model that, despite homonymous nomenclatures, have
different meanings (Rodrigues et al. 2018). Inserting a higher-level semantic layer could increase
the syntropy of the proposed model.
6 Conclusion and Future Work
The context of analysis of data collected from OSN services is already a research reality in
several areas of knowledge, such as Information Science, Sociology, and Computer Science.
However, the context proposed in this research will still require more studies on the adaptation of
existing tools and greater detail on the possibility of using measurable elements and, therefore,
qualifiable to support hypotheses about data collection by external agents and to justify diagnoses
of possible actions that could potentially harm the privacy of personal data.
The model aims to systematize and contribute with reference structures to assist analysis
of the sharing of personal data collected by OSN services with third-party organizations. One of
the results of this research – the model for analyzing the data collected from OSN services – could
contribute to clarifying elements that allow a more structured perception, including the possibility
of monitoring changes in the API by organizations over time. It is relevant to explain that, in
analogy to the object-oriented, this model is closer to the definition of classes than to instantiated
data. In this sense, its application is suitable for structural analysis but not for the data obtained in
each profile in each OSN service.
Despite the relevance of the proposed model, the assignment of analyzing the structures of
networks (especially social networks) still lacks new components that, from demands generated
by public scrutiny, will emerge over the next few years (including possible consequences
elucidated in this research).
6
RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
Notes
(1) This work was supported in part by a grant from Brazilian Coordination for the Improvement of Higher Education
Personnel – CAPES Foundation.
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Research Data
You can find all data used in the research in the Tables and Figures of this document. Additional information
may be made available upon request by email.
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Appendix A
Table 1 – The Data Dictionary for the Entity-Relationship Model about levels to provide personal data in
OSN services.
Table Name
Attribute Name
Domain
(Size)
Description
Integrity
constraints
osns
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
name
Text(65,535)
The name of Online Social Network service.
Not Null
Unique
url
Text(65,535)
The main Uniform Resource Location of the
Online Social Network Service.
Not Null
Unique
api
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
osns_id
Integer(4)
The foreign key of the relationship between
osns and api tables.
Foreign Key
Not Null
name
Text(65,535)
The name of Application Programming
Interface.
Not Null
Unique
api_version
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
api_id
Integer(4)
The foreign key of the relationship between
api and api_version tables.
Foreign Key
Not Null
version_number
Varchar(200)
The API version number.
Not Null
Unique
date_launch
Date(10)
The API version working start date.
Not Null
date_deprecated
Date(10)
The API version deprecated date.
description
Text(65,535)
When available, the original description of
the API version, without interpretation from
the data collector.
url
Text(65,535)
The main Uniform Resource Location to
access the reference document of the API
version.
Not Null
authorization
_access
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
api_id
Integer(4)
The foreign key of the relationship between
api and authorization_access tables.
Foreign Key
Not Null
api_version_id
Integer(4)
The foreign key of the relationship between
api_version and authorization_access tables.
Foreign Key
Not Null
name
Varchar(200)
The given name of the Access Authorization.
Not Null
Unique
description
Text(65,535)
When available, the original description of
the authorization access, without
interpretation from the data collector.
url
Text(65,535)
The main address to access the document
about the authorization access, in Uniform
Resource Locator (URL) format.
Not Null
permission
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
api_id
Integer(4)
The foreign key of the relationship between
api and permission tables.
Foreign Key
Not Null
api_version_id
Integer(4)
The foreign key of the relationship between
api_version and permission tables.
Foreign Key
Not Null
name
Varchar(200)
The given name of the permission.
Not Null
12
RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
Table Name
Attribute Name
Domain
(Size)
Description
Integrity
constraints
Unique
description
Text(65,535)
When available, the original description of
the permission, without interpretation from
the data collector.
url
Text(65,535)
The main address to access the document
about the permission, in Uniform Resource
Locator (URL) format.
Not Null
view
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
api_id
Integer(4)
The foreign key of the relationship between
api and view tables.
Foreign Key
Not Null
api_version_id
Integer(4)
The foreign key of the relationship between
api_version and view tables.
Foreign Key
Not Null
name
Varchar(200)
The original name of the View as described in
the reference documentation.
Not Null
Unique
description
Text(65,535)
When available, the original description of
the view, without interpretation from the data
collector.
url
Text(65,535)
The main address to access the document
about the view, in Uniform Resource Locator
(URL) format.
Not Null
attribute
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
view_id
Integer(4)
The foreign key of the relationship between
view and attribute tables.
Foreign Key
Not Null
data_type_id
Integer(4)
The foreign key of the relationship between
attribute and data_type tables.
Foreign Key
Not Null
data_value_id
Integer(4)
The foreign key of the relationship between
attribute and data_value tables.
Foreign Key
Not Null
name
Varchar(200)
The original name of the attribute as
described in the reference documentation.
Not Null
Unique
description
Text(65,535)
When available, the original description of
the attribute, without interpretation from the
data collector.
relation
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
view_source_id
Integer(4)
The foreign key of the relationship between
relation and view (source) tables.
Foreign Key
Not Null
cardinality_source_
id
Integer(4)
The foreign key of the relationship cardinality
of the view (source).
Foreign Key
Not Null
view_destination_i
d
Integer(4)
The foreign key of the relationship between
relation and view (destination) tables.
Foreign Key
Not Null
cardinality_destinat
ion_id
Integer(4)
The foreign key of the relationship cardinality
of the view (destination).
Foreign Key
Not Null
name
Varchar(200)
The name of the relationship.
Not Null
Unique
request
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
view_id
Integer(4)
The foreign key of the relationship between
view and request tables.
Foreign Key
Not Null
data_format_id
Integer(4)
The foreign key of the relationship between
request and data_format tables.
Foreign Key
Not Null
name
Varchar(200)
The name of the request.
Not Null
13
RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
Table Name
Attribute Name
Domain
(Size)
Description
Integrity
constraints
Unique
protocol
Text(65,535)
Name of the protocol used to make the
request and collect the data.
Not Null
description
Text(65,535)
When available, the original description of
the request, without interpretation from the
data collector.
parameter
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
data_type_id
Integer(4)
The foreign key of the relationship between
parameter and data_type tables.
Foreign Key
Not Null
data_value_id
Integer(4)
The foreign key of the relationship between
parameter and data_value tables.
Foreign Key
Not Null
name
Varchar(200)
The given name of the parameter.
Not Null
description
Text(65,535)
When available, the original description of
the parameter, without interpretation from the
data collector.
qualifier
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
name
Varchar(200)
The original qualifier name, as described in
the reference documentation.
Not Null
description
Text(65,535)
When available, the original description of
the qualifier, without interpretation from the
data collector.
Not Null
url
Text(65,535)
The main address to access the document
about the qualifier, in Uniform Resource
Locator (URL) format.
Not Null
cardinality
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
name
Char(6)
The cardinality name, which can be One-to-
one, One-to-Many, and Many-to-Many.
Not Null
Unique
description
Text(65,535)
The description of cardinality.
Not Null
data_format
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
name
Varchar(200)
The format name in which data can be
collected.
Not Null
Unique
description
Text(65,535)
The data format description.
Not Null
data_type
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
name
Varchar(200)
The name of the data type.
Not Null
Unique
description
Text(65,535)
The description of the characteristics of the
data type.
Not Null
data_value
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
name
Varchar(200)
The name of the Data Value.
description
Text(65,535)
The description of the characteristics of the
expected value type for each data.
Not Null
relation_type
id
Integer(4)
The artificial ID to the table, automatically
generated (auto-increment).
Primary Key
Not Null
name
Varchar(20)
The name of the type of relationship between
views.
Not Null
Unique
description
Text(65,535)
A description of the characteristics of the type
of relationship between views.
Not Null
14
RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
Table Name
Attribute Name
Domain
(Size)
Description
Integrity
constraints
authorization
_access_attri
bute
authorization_acce
ss_id
Integer(4)
The foreign key represents the relationship
between the authorization_access and the
attribute tables with id value from the
authorization_access table.
Primary Key
Not Null
attribute_id
Integer(4)
The foreign key represents the relationship
between the authorization_access and the
attribute tables with id value from the
attribute table.
Primary Key
Not Null
authorization
_access_per
mission_attri
bute
authorization_acce
ss_id
Integer(4)
The foreign key represents the relationship
between the authorization_access,
permission, and attribute tables with id value
from the authorization_access table.
Primary Key
Not Null
permission_id
Integer(4)
The foreign key represents the relationship
between the authorization_access,
permission, and attribute tables with id value
from the permission table.
Primary Key
Not Null
attribute_id
Integer(4)
The foreign key represents the relationship
between the authorization_access,
permission, and attribute tables with id value
from the attribute table.
Primary Key
Not Null
authorization
_access_per
mission_vie
w
authorization_acce
ss_id
Integer(4)
The foreign key represents the relationship
between the authorization_access,
permission, and view tables with id value
from the authorization_access table.
Primary Key
Not Null
permission_id
Integer(4)
The foreign key represents the relationship
between the authorization_access, permission,
and view tables with id value from the
permission table.
Primary Key
Not Null
view_id
Integer(4)
The foreign key represents the relationship
between the authorization_access, permission,
and view tables with id value from the view
table.
Primary Key
Not Null
authorization
_access_vie
w
authorization_acce
ss_id
Integer(4)
The foreign key represents the relationship
between the authorization_access and view
tables with id value from the
authorization_access table.
Primary Key
Not Null
view_id
Integer(4)
The foreign key represents the relationship
between the authorization_access and view
tables with id value from the view table.
Primary Key
Not Null
attribute_qua
lifier
attribute_id
Integer(4)
The foreign key represents the relationship
between the attribute and qualifier tables with
id value from the attribute table.
Primary Key
Not Null
qualifier_id
Integer(4)
The foreign key represents the relationship
between the attribute and qualifier tables with
id value from the qualifier table.
Primary Key
Not Null
parameter_q
ualifier
parameter_id
Integer(4)
The foreign key represents the relationship
between the parameter and qualifier tables
with id value from the parameter table.
Primary Key
Not Null
qualifier_id
Integer(4)
The foreign key represents the relationship
between the parameter and qualifier tables
with id value from the qualifier table.
Primary Key
Not Null
request_para
meter
request_id
Integer(4)
The foreign key represents the relationship
between the request and parameter tables with
id value from the request table.
Primary Key
Not Null
parameter_id
Integer(4)
The foreign key represents the relationship
between the request and parameter tables with
id value from the parameter table.
Primary Key
Not Null
direction
Varchar(7)
The parameter direction at the time of the
request. It can be a) input, b) output and c)
input and output.
Not Null
Source: Authors.
15
RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar Gonçalves. Privacy and Online Social Network: a
model for analysis of collecting personal data. Brazilian Journal of Information Science: research trends, vol.
17, publicação contínua, 2023, e023005. DOI: 10.36311/1981-1640.2023.v17.e023005
Copyright: © 2023. RODRIGUES, Fernando de Assis; SANT'ANA, Ricardo Cesar. This is an open-access
article distributed under the terms of the Creative Commons CC Attribution-ShareAlike (CC BY-SA),
which permits use, distribution, and reproduction in any medium, under the identical terms, and provided
the original author and source are credited.
Received: 11/11/2022 Accepted: 10/01/2023