#  1\. Introduction

SECONDO aims at achieving the following features simultaneously: efficiency,
security, user privacy, and flexibility of contract expressiveness. This
deliverable addresses the main elements of the Data Management policy that
will be used by the project participants regarding all Datasets. It also
establishes some procedural mechanisms for participants with the
responsibilities of Data Controllers and Processors. Through the SECONDO
project, **Data Controllers** and **Processors** are defined as below

[4] :

  * **Data Controller** means the natural or legal person, public authority, agency or other body which, alone or jointly with others, determines the purposes and means of the processing of personal data; where the purposes and means of such processing are determined by Union or Member State law, the controller or the specific criteria for its nomination may be provided for by Union or Member State law; 

  

  * **Data Processor** means a natural or legal person, public authority, agency or other body which processes Personal Data on behalf of the controller; 

  

The DMP establishes a set of guidelines meeting each of the fundamental topics
to be considered. These guidelines cover aspects such as applicable policies,
roles, standards, infrastructures, sharing strategies, Data processing,
storage, retention and structure, legal compliance and compliance with market
standards and best ethical and privacy practices, identification,
accessibility, intelligibility, legitimate use for other purposes. These
guidelines will be adopted at the early stages of the project.

For each Dataset in H2020 the following aspects should be considered:

  * Making Data **F** indable, 

  

  * Making Data openly **A** ccessible, 

  

  * Making Data **I** nteroperable, 

  

  * Increase Data **R** e-use, 

  

  * Allocation of recourses and Data security 

  

The Data collected/generated during the project will be owned by the partners
which have contributed to producing that Data (Data controller). The extent up
to which this Data will be made available and which restrictions will be
imposed on its re-use will be decided on a case-by-case basis by the Data
controller. Moreover, Data controller determines the purposes and means of
Personal Data processing and will decide the purpose for which Personal Data
is required and what Personal Data is necessary to fulfil that purpose.

The partners will comply with the Findable, Accessible, Interoperable,
Reusable (FAIR) guidelines of the H2020 programme, which state that Data will
be made as available as possible, so long that does not negatively affect the
commercial advantage of the partners. The Horizon 2020 FAIR DMP template [5]
has been designed to be applicable to any Horizon 2020 project that produces,
collects or processes research Data.

The Data will be shared among partners using internal repositories or through
direct communication, and with the public through the project’s website or
public repositories. The Data will be preserved up to **three (3) years**
after the end of the project at the partners’ repositories and cloud
infrastructures, according to each partner’s internal policy.

SECONDO DMP should be updated as a minimum in time with the periodic
evaluation/assessment of the project. Furthermore, the consortium can define a
timetable for review in the DMP itself. Regarding [5], the SECONDO DMP needs
to be updated over the course of the project whenever significant changes
arise, such as (but not limited to):

  * Using new Dataset 

  

  * Changes in consortium policies (e.g. innovation potential, decision to file for a patent) 

  

  * Changes in consortium composition and external factors (e.g. new consortium members joining or old members leaving). 

  

Regarding  _Participant Portal H2020 Online Manual_ [6], as part of making
research Data findable, accessible, interoperable and re-usable (FAIR),
SECONDO FAIR DMP should include information on:

  * The handling of research Data during and after the end of the project  What Data will be collected, processed and/or generated? 

  

  * Which methodology and standards will be applied? 

  

  * Whether Data will be shared/made open access? 

  

  * How Data will be curated and preserved (including after the end of the project). 

  

The SECONDO FAIR Dataset Template Questionnaire (Table 6-1) includes a set of
questions that all Data controllers are required to fill in for each Dataset
[3], [7], [8]. The questionnaire template has been reviewed by the Project
Coordinator (UPRC) and the Ethics Board for completeness and compliance with
the FAIR DMP directives.

Zenodo [9] will be used as the project Data and publication repository and
will be linked to the SECONDO project-site at OpenAIRE. Zenodo is a simple and
innovative service that enables researchers, scientists, EU projects and
institutions to share and showcase multidisciplinary research results (Data
and publications) that are not part of existing institutional or subject-based
repositories.

#  2\. SECONDO FAIR Data Principles

##  2.1 DATA Summary

As part of making research data Findable, Accessible, Interoperable and Re-
usable (FAIR), a DMP should [6]:

**To be Findable:**

  * Data/Metadata are assigned a globally unique and eternally persistent identifier. 

  

  * Data are described with rich Metadata. 

  

  * Data/Metadata are registered or indexed in a searchable resource.  Metadata specify the Data identifier. 

  

**To be Accessible:**

  * Data are retrievable by their identifier using a standardized communications protocol. 

  

  * the protocol is open, free, and universally implementable. 

  

  * the protocol allows for an authentication and authorization procedure, where necessary.  Metadata are accessible, even when the Data are no longer available. 

  

_Particularly, SECONDO Database will be accessible through the SECONDO project
for**three (3)** years following the end of the project. During this period,
unless otherwise decided by the consortium members, the Database functionality
will remain the same as during the project duration. _

**To be Interoperable:**

  * Data/Metadata use a formal, accessible, shared, and broadly applicable language for knowledge representation. 

  

  * Data/Metadata use vocabularies that follow FAIR principles. 

  

  * Data/Metadata include qualified references to other Data/Metadata. 

  

_There is not a standard for allowing Data exchange between researchers,
institutions, organizations, countries, etc. (e.g. adhering to standards for
Data annotation, Data exchange, compliant with available software
applications, and allowing re-combinations with different Datasets from
different origins). Thus, it always needs of a human interpretation of the
Data structure to manually create a Data map. However, the utilization of
standards for Data capturing and the documented annotation will ease the Data
exchange._

**To be Re-usable:**

  * Data/Metadata have a plurality of accurate and relevant attributes. 

  

  * Data/Metadata are released with a clear and accessible Data usage license. 

  

  * Data/Metadata are associated with their provenance. 

  

  * Data/Metadata meet domain-relevant community standards. 

  

_SECONDO Data will be licensed under Creative Commons license, to the extent
that it may be subject to such licensing (likely the “CC BY”). Applicable Data
will become available at the end of the project. The Data can be re-used by
other scientists and interested parties. Parts of the Data may become
available prior to this as a result of journal publications. There will be no
embargo period._

##  2.1.1 Purpose of the Data Collection/Generation and its relation to the
objectives of the project

SECONDO will propose a unique, scalable, highly interoperable **Economics-of-
Security-as-a-Service (ESaaS) platform** that encompasses a comprehensive
cost-driven methodology for estimating cyber risks and determining the
residual risks. The SECONDO platform will establish a new paradigm in risk
management for enterprises of various sizes, with respect to the GDPR
framework, while it will enable formal and verifiable methodologies for
insurers that require estimating premiums. **SECONDO will not collect or
process Personal Data to conduct its research. The collection, processing and
use of Personal Data is only admissible if expressly permitted by any legal
provision or if the Data subject has expressly consented in advance.**

###  2.2 Allocation of Resources

The costs for Data preparation to be FAIR are unknown at this stage but will
be estimated in the future. Expenses may consist of additional publication and
documentation costs of the repositories where applicable. Data preparation and
management costs during the project will be covered by the project.

**UPRC** , as the Project Coordinator for SECONDO, will be responsible for DMP
updates, and Data archiving and publication within repositories. No additional
funding is provided for Data management activities for those deciding to
participate in the pilot. Costs relating to open access to research Data will
be eligible as part of the grant, independent from the participation in the
pilot, provided the general eligibility conditions specified in the Grant
Agreement are followed.

###  2.3 Data Sharing

The Data controller will determine the details of how Data will be shared,
including access procedures, embargo periods (if any), outlines of technical
mechanisms for dissemination and necessary software and other tools for
enabling re-use, and definition of whether access will be widely open or
restricted to specific groups. Similarly, the Data controller will identify
the repository where Data will be stored, if already existing and identified,
indicating in particular the type of repository (institutional, standard
repository for the discipline, etc.).

During the project, any potential user that wants the get access would be
guided to:

  * Submit a "Request" to the Dataset controller from the SECONDO consortium. This request will contain: 

    * Full name 

  

    * Organization and department o Email address 

  

    * Description of intended use 

  

  

  * After reviewing the request, if the Data controller approves it, the user will receive an email with a special link to verify the email address. 

  

  * Then the user is asked to agree to and sign the following terms of access: 

  

[RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the
Dataset. In exchange for such permission, Researcher hereby agrees to the
following terms and conditions:

  * Researcher shall use the Database only for non-commercial research and educational purposes. 

  

  * Data Controller makes no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose. 

  

  * Researcher accepts full responsibility for his or her use of the Dataset and shall defend and indemnify Data Controller, including their employees, trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset, including but not limited to Researcher's use of any copies of copyrighted Dataset that he or she may create from the Dataset. 

  

  * Researcher may provide research associates and colleagues with access to the Dataset provided that they first agree to be bound by these terms and conditions. 

  

  * Data Controller reserves the right to terminate Researcher's access to the Database at any time and without justification. 

  

  * If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 

  

  * The law and jurisdiction of the Data Controller’s country shall apply to all disputes under this agreement. 

  

##  2.3.1 Methods for Data sharing

The methods used to share Data will be dependent on a number of factors such
as the type, size, complexity and sensitivity of Data. Data can be shared by
any of the following methods [10]:

  * **Under the auspices of the Principal Investigator**

  

Investigators sharing under their own auspices may securely send Data to a
requestor or upload the Data to their institutional website. Investigators
should consider using a Data-sharing agreement to impose appropriate
limitations on the secondary use of the Data _._

  * **Through a third party**

  

Investigators can share their Data by transferring it to a Data archive
facility to distribute more widely to the scientific community, to maintain
documentation and meet reporting requirements. Data archives are particularly
attractive for investigators concerned about managing a large volume of
requests for Data, vetting frivolous or inappropriate requests, or providing
technical assistance for users seeking to help with analyses.

  * **Using a Data enclave**

  

Datasets that cannot be distributed to the general public due to
confidentially concerns, or third-party licensing or use agreements that
prohibit redistribution, can be accessed through a Data enclave. A Data
enclave provides a controlled secure environment in which eligible researchers
can perform analyses using restricted Data resources.

  * **Through a combination of methods**

  

Investigators may wish to share their Data by a combination of the above
methods or in different versions, in order to control the level of access
permitted.

_**Note:** During the SECONDO project, Data controllers could use **a
combination of methods** for Data sharing. _

###  2.4 Data Security

Regardin  g _Guide on Good Data_ _protection practice_ [  11], to process Data
in a secure manner, each Data controller must:

  * Take technical and organisational measures to prevent any unauthorised access  Establish clear access rules 

  

  * Organise the processing in a way that gives you the best possible control, for example by allowing for tracking of access (logbook) 

  

  * If someone processes the Data on your behalf, make sure that this processor ensures for appropriate security safeguards. 

  

In practical terms, these measures could result in:

  * User authentication: The way to verify the identity of a user 

  

  * Access control: Mechanism to allow or deny access to certain Data 

  

  * Storage security: Storing Data in a way that prevents unauthorised access, for example by: 

    * Operating system controls (authentication & access control) 

  

    * Use of passwords to access electronic files (e.g. use the text editor function to save a document password-protected) 

  

    * Local encrypted storage (enable the full disk encryption, enable the file system, enable the text editor encryption) 

  

    * Database encryption: turning Data into a form that makes them unintelligible (for anyone not having access to the key) 

  

  

  * Communication security: Safe electronic communication for transferring the Data can take the following forms: 

    * Encrypted communication (SSL/TLS); (e.g., use web services whose URL starts with 

  

  

‘https: //’ and not only http ://) o Firewall systems and access control lists
(e.g. make sure the firewall service is enabled on your PC)

  * Anti-virus & anti-malware systems 

  

  * Protect Data and Data carriers when they are physically transferred (paper notes, laptop etc.). 

  

  * Other IT technical controls such as installing security updates, anti-virus protection, local backups, blocking of certain software installation, etc. 

  

Regarding the guidelines on implementation of open access to scientific
publications and research Data, participants of the ORDP need to take the
following three steps [12]:

  * Deposit research Data needed to validate the results presented in scientific publications, including associated Metadata, in the repository as soon as possible. Also, other Data (for instance Data not directly attributable to a publication, or raw Data), including associated Metadata, should be deposited – that is, according to the individual judgement by each project, specified in the Data management plan. 

  

  * Take measures to enable third parties to access, mine, exploit, reproduce and disseminate 

  

(free of charge for any user) this research Data, for instance by attaching
_a Creative Commons_ _Attribution Licence_ (CC BY) to the Data deposited, or
by waiving all interests associated to copyright and Database protection.

  * Provide information via the chosen repository about the tools available in order for the beneficiaries to validate the results, e.g., specialised software or software code, algorithms and analysis protocols. Where possible, these tools or instruments should be provided. 

  

All SECONDO software/toolkit modules will encapsulate state-of-the art
security, authentication and authorization mechanisms. The robustness of such
modules is ensured by years of developments in the field (the basic building-
blocks stem from previously funded EU projects or from already functioning
commercial solutions) and will be tested through dedicated penetration /
hacking tests and challenges. In addition, Data protection methods will be
made available through a set of secure APIs and Smart Contracts. Moreover,
privacy-preserving smart contracts will be leveraged to hide sensitive client
information and meanwhile, secure encryption technique will be considered in
Data storage.

Privacy-preserving techniques will be used in Data storage and smart contract
to protect clients’ privacy. Privacy-preserving smart contracts will be
leveraged to hide sensitive client information and meanwhile, secure
encryption technique will be considered in Data storage.

The conceptual security and privacy taxonomy will be applied. It contains
three main Big Data security and privacy principles:

  * Data confidentiality topic: safeguarding the confidentiality of Personal Data. 

  

  * Data provenance topic: safeguarding the integrity and validation of Personal Data. 

  

  * Public policy, social, and cross-organizational topics: safeguarding the specific Big Data and privacy and Data protection requirements. 

  

In SECONDO, a Byzantine-fault-tolerance-like algorithm will be used to
randomly select a group of clients as validators. To achieve security, access
control will be used to guarantee that only registered clients can read
information from the ledger.

##  2.4.1 Data Protection

**As mentioned in SECONDO DOA, no real Data will be used in the context of the
project. However, with SECONDO being a GDPR-compliant platform by design, we
describe the procedures and technical measures that would be applied if real
Data are being processed.**

A key issue in considering observational research using social media is
whether the proposed project meets the criteria as human subjects’ research,
and if so, what type of review is needed. A human subject is defined by
federal regulations as a living individual about whom an investigator obtains
Data through interaction with the individual or identifiable private
information [13].

An important area of concern with **Social Media Website (SMW)** research is
the protection of confidentiality. Similar to other types of research
involving survey or interview Data, protection of participant identities is
critical. Website research may initially be perceived as lower risk, because
participant information can be collected in absence of some protected
information such as address or phone number. Online Data can present increased
risks; studies that publish direct text quotes from an SMW may directly
identify participants. Entering a direct quote from an SMW into a Google
search engine can lead to a specific Web link, such as a link to that person’s
LinkedIn profile, and thus identify the participant.

Personal Data refers to any information relating to an identified or
identifiable natural person, meaning by identifiable person the one who can be
identified, directly or indirectly, in particular by reference to an
identification number or to one or more factors specific to his physical,
physiological, mental, economic, cultural or social identity. Data is
considered personal when someone is able to connect information to a specific
person, even when the person or entity that is holding the Personal Data
cannot make the connection directly (e.g. name, address, e-mail), but has or
may have access to information allowing such identification (e.g. through
telephone numbers, credit card numbers, license plate numbers, etc.).

The fundamental right to the protection of Personal Data is explicitly
recognised in Article 8 of the Charter of Fundamental Rights of the European
Union, and in Article16 of the Treaty on the functioning of the European
Union, according to which everybody has the right to the protection of
Personal Data concerning them. Such Data must be processed fairly for
specified purposes and on the basis of the consent of the person concerned or
some other legitimate basis laid down by law.

If Data controllers intend to process sensitive data in the project, or if
there is a possibility that sensitive data (See section: 5: Ethical Aspects
and Privacy and Security Requirements) may be processed (unintended processing
of sensitive data), more solid justification to the Ethics Committee have to
be provided by Data Controllers

SECONDO Data processing must be lawful, fair and transparent. It should
involve only Data that are necessary and proportionate to achieve the specific
task or purpose for which they were collected. Therefore, **SECONDO will only
collect the Data that is needed for the research objectives** , since
collecting unnecessary/unrelated Data for the research project may be deemed
unethical and unlawful. The Data are to be processed only for scientific
purposes comprising processing operations that are performed for purposes of
study and systematic research to develop scientific knowledge for the specific
sector addressed by SECONDO.

**SECONDO will not collect or process Personal Data to conduct its research.
Any real users that will take part in the assessment of the implemented
software do not have to provide Personal Data and in case some non-sensitive
Data are needed, the users will be informed and sign the appropriate consent
and agreements.**

To secure the confidentiality, accuracy, and security of Data management, the
following measures will be taken:

  * All Personal Data obtained in SECONDO studies will be transmitted to partners within the consortium only after anonymization. Keys to identification numbers will be held confidentially within the respective research units. In situations were re-identification of study participants becomes necessary, for example the collection of additional Data, this will only be possible through the research unit and in cases where informed consent for such cases has been given. 

  

  * Personal Data are entered to secure websites. Data are processed only for the purposes outlined in the patient information and informed consent forms of the respective case studies. 

  

Use for other purposes will require explicit patient approval. Also, Data are
not transferred to any places outside the consortium without patient consent.

  * None of the Personal Data will be used for commercial purposes, but the knowledge derived from the research using the Personal Data may be brought forward to such use as appropriate, and this process will be regulated by the Grant Agreement and the Consortium Agreement, in accordance with any generally valid legislation and regulations. 

  

  * No vulnerable or high-risk groups are used (e.g. children, adults unable to consent, people in dependency relationship, vulnerable persons) will be addressed during the development and progress of the SECONDO project; 

  

  * Persons are approached in their professional capacity; 

  

  * The purpose of collecting contact Data of potential stakeholders is to ask them about their willingness to be involved in SECONDO network and for obtaining professional opinions and consultation only; 

  

  * Information about the objectives of the project, structuring of the Stakeholder Network and details about Data processing will be provided in advance (as a governance document) to all external stakeholders; 

  

  * Minimum and limited amount of Personal Data will be collected; 

  

  * Personal contact Data will be kept internally within the SECONDO partners and will not be accessible to external organizations or individuals. 

  

  * Personal Data shall always be collected, stored, and exchanged in a secure manner, through secure channels during the project. 

  

Regarding Data confidentiality, SECONDO partners must keep any Data, documents
or other material confidential during the implementation for the project and
for three years after end of the project.

_**Note** _ : Appendix (Table 8-1, Table 8-2, Table 8-3) has to be filled by
the SECONDO Data controllers.

#  3\. SECONDO Data Sourcing and Data Sharing

In general, Data may be grouped into four main types based on methods for
collection:

  * Observational Data: captured in real time, typically cannot be reproduced exactly. 

  

  * Experimental Data: from labs and equipment, can often be reproduced. 

  

  * Simulation Data: from models, can typically be reproduced if the input Data is known. 

  

  * Derived or Compiled Data: after Data mining or statistical analysis has been done, can be reproduced if analysis is documented. 

  

The categories of Data processed in SECONDO are:

  * Experimental 

  

_Dataset captured from a real infrastructure, such as external sources e.g.
social media and other internet-based sources, including Darknet to establish
research activities with a static dataset._

  * Simulation 

  

_Dataset captured in real time from a testbed or lab infrastructure to monitor
it and test optimization strategies from internal organisation sources, e.g.
network infrastructure._

  * Derived or Compiled Data 

  

_The intelligent**Big Data Collection and Processing Module (BDCPM)** uses
specialised crawlers to acquire risk-related Data. _

##  3.1 Overview of Research Objectives (ROs’) scenarios

The interactions mapped in the Research Objectives (ROs) scenarios have
determined the Data sources, as well as the connections that will take place
in SECONDO. A short description of each ROs’ scenarios can be found below:

**RO1. Design and develop an extended risk analysis metamodel.**

One of the key contributions of the SECONDO programme in the area will be the
design, analysis and implementation of a Quantitative Risk Analysis Metamodel
(QRAM) that will utilise advanced security metrics to quantitatively estimate
the exposed cyber risks. To implement the desired functionalities the
following SECONDO modules will be implemented:

  * **Risk Analysis Ontology and Harmonisation Module (RAOHM)**

  

_RAOHM receives the outcomes of the existing risk analysis tools and
harmonises them using a common vocabulary with straightforward definition in
order to be used by QRAM (Leader: UPRC)_

  * **Social Engineering Assessment Module (SEAM)**

  

_SEAM interacts with users to devise their behaviour using penetration testing
approaches and it provides specific numeric results on risky actions, (i.e.
percentage of users that open suspect files or execute Trojans, etc.) (Leader:
UPRC)._

  * **Intelligent Big Data Collection and Processing Module (BDCPM)**

  

_BDCPM uses specialised crawlers to acquire risk-related Data either from
internal organisation sources, e.g. network infrastructure or external sources
such as social media and other internet-based sources, including Darknet.
(Leader: LST)_

**RO2. Design and develop a scenario-based risk management module that
facilitates in both cost-effective risk management and optimised security
investments.**

Cyber Security Investment Module (CSIM) will be designed and implemented. CSIM
will provide decision support for organisations that seek an optimal
equilibrium point (i.e. balance) between spending on cyber security investment
and cyber insurance fees.

CSIM will use the following results/procedures/modules outcome as an input:

  * Costs for attacking and defending will be investigated and they will be given as an input to CSIM. (Leader: CUT) 

  

  * the outcome of the provided extended and QRAM 

  

  * the results of BDCPM that provides analytics on Internet sources regarding state-of-the-art security solutions as well as their cost. (Leader: LST) 

  

  * The outcome of the Game Theoretic Module (GTM) that models all possible attacking scenarios and defensive strategies, (i.e. available security controls), by employing attack graphs (Leader: FOGUS) 

  

  * The outcome of the Econometrics Module (ECM) that provides estimates of all kinds of costs of potential attacks and it takes into account costs, (i.e. purchase, installation, execution, etc.), of each possible security control using a set of existing econometric models; (Leader: CUT) 

  

  * The outcome of the Continuous Risk Monitoring Module (CRMM) that assesses on a continuous basis the performance of the implemented risk-reducing cyber security controls allowing the adaptation of the cyber insurance contract to the changing IT environment and the evolving cyber threat landscape (Leader: UBI) 

  

**RO3. Design and develop a cyber insurance module that estimates cyber
insurance exposure and derives coverage and premiums.**

the Cyber Insurance Coverage and Premiums Module (CICPM) will compute premium
curves and coverages as a function of the organisation’s security level (can
be used by clients). CICPM will communicate with CRMM for monitoring the
conditions that violate cyber insurance contract agreements toward resolving
conflicts.

CICPM will use the following results/outcomes/policies as an input to propose
the insurance calculation tool:

  * The outcome of the proposed QRAM. 

  

  * The defending policies selected to be applied in order to provide optimal protection strategies as well as the results of the related econometric parameters that justify the cost effectiveness of the considered security investments (Leader: UPRC). 

  

  * The results of analytics on cyber insurance environment and market (Leader: CRO). 

  

  * The underwriter’s strategy ( Leader: SURREY). 

  

**RO4. Use smart contracts and a blockchain to empower cyber insurance
claim.**

SECONDO will deploy a blockchain, which is a distributed decentralised
Database that maintains continuously growing blocks of Data records, in which
all blocks are tightly chained together against information tampering. SECONDO
will use a private ledger, which provides secure access control on Data
records, to hold an inventory of assets and information regarding security and
privacy risk measurable indicators of an organisation (cyber insurance
client). The ledger will be updated based on information received from CRMM.
By using smart contracts, the traditional physical-based paper process and
endorsement will be turned to digital formats that brings convenience on Data
management (Leader: SURREY).

##  3.2 SECONDO Data Sources

**In the context of the project, SECONDO will not collect /process Personal
Data to conduct its research.** The major Data sources, as these have been
identified in SECONDO Description Of Action (DOA), are described below.

UPRC and LIST are nodes of QRAM that RAOHM (Leader: UPRC) as a main part of
SECONDO Risk analysis module, receives the outcomes of the existing risk
analysis tools and harmonises them using a common vocabulary with
straightforward definition. And internal organisation sources, e.g. network
infrastructure or external sources such as social media and other internet-
based sources, including Darknet will be used by BDCPM to acquire risk-related
Data. In the context of the QRAM, SEAM (Leader: UPRC) interacts with users to
devise their behaviour using penetration testing approaches and it provides
specific numeric results on risky actions, (i.e. percentage of users that open
suspect files or execute Trojans, etc.)

For the CSIM phase, costs for attacking and defending will be investigated and
they will be given as an input to CSIM (Leader: CUT), and the results of BDCPM
that provides analytics on Internet sources regarding state-of-the-art
security solutions as well as their cost. (Leader: LST). The outcome of the
Game Theoretic Module (GTM) that models all possible attacking scenarios and
defensive strategies, (i.e. available security controls), by employing attack
graphs (Leader: FOGUS).

ECM provides estimates of all kinds of costs of potential attacks and it takes
into account costs, (i.e. purchase, installation, execution, etc.), of each
possible security control using a set of existing econometric models; (Leader:
CUT). CRMM assesses on a continuous basis the performance of the implemented
risk-reducing cyber security controls allowing the adaptation of the cyber
insurance contract to the changing IT environment and the evolving cyber
threat landscape (Leader: UBI).

CICPM will compute premium curves and coverages as a function of the
organisation’s security level (can be used by clients). CICPM will communicate
with CRMM for monitoring the conditions that violate cyber insurance contract
agreements toward resolving conflicts. CICPM will use the QRAM’s outcome,
Cyber insurance ontology (Lead: UPRC), results of analytics on cyber insurance
environment and market (Leader: CRO), the underwriter’s strategy (Leader:
SURREY).

As mentioned before, SECONDO will use a private ledger to hold an inventory of
assets and information regarding security and privacy risk measurable
indicators of an organisation (cyber insurance client). By using smart
contracts, the traditional physical-based paper process and endorsement will
be turned to digital formats that brings convenience on Data management
(Leader: SURREY).

#  4\. Data Archiving and Preservation (including storage and backup)

The collected Data will be stored in secure servers, only accessible to the
consortium members. If any identifiable Data are required for the research
purposes, access to and distribution of this Data will be granted only after
explicit permission and after the agreement of the user participants.
Authentication will be required to access stored Data on the research site.
Authorized consortium members will have access to the Data after
authentication with a centralized server and on a need to know basis.
Consortium members will have access rights to add Data to the identity
Database. No editing or reading rights will be granted to them to prevent
alteration/disclosure of private Data, if a specific permission is not granted
by the respective user participant.

All technical partners participating in SECONDO have previous experience in
storing and processing user Data. This implies that all of them have the
appropriate competence and infrastructure to address the processing of SECONDO
user Data. This will assure secure storage, delivery and access of Personal
Data, as well as managing the rights of the users. In this way, there is
complete guarantee that the accessed, delivered, stored and transmitted
content will be managed by the right persons, with welldefined rights, at the
right time.

Depending on each Dataset the Data archiving and preservation procedures that
will be put in place for long-term preservation of the Data will be
responsibility of the corresponding Data Controller. This includes the
indication of how long the Data should be preserved, what is its approximated
end volume, what the associated costs are and how these are planned to be
covered. As mentioned before, privacy-preserving smart contracts will be
leveraged to hide sensitive client information and meanwhile, secure
encryption technique will be considered in Data storage.

#  5\. Ethical Aspects and Privacy and Security Requirements

Privacy and Data protection are fundamental rights, which needs to be
protected. Privacy can mean different things in different contexts and
cultures. It is therefore important to detail the purpose of the research
according to the different understandings of privacy. In the context of
research, privacy issues arise whenever data relating to persons are collected
and stored, in digital form or otherwise. The main challenge for research is
to use and share the data, and at the same time protect personal privacy.
Moreover, Data protection aims at guaranteeing the individual’s right to
privacy. It refers to the technical and legal framework designed to ensure
that Personal Data are safe from unforeseen, unintended or malevolent use.
Data protection therefore includes e.g., measures concerning collection,
access to Data, communication and conservation of Data. In addition, a Data
protection strategy can also include measures to assure the accuracy of the
Data. In the context of research, privacy issues arise whenever Data relating
to persons are collected and stored, in digital form or otherwise. The main
challenge for research is to use and share the Data, and at the same time
protect personal privacy [7]. In order to ensure respect for Data protection
and privacy, the European University Institute (EUI) has adopted a Data
Protection Policy [14] that must be respected by all EUI members and which is
inspired by the EU Data protection rules. If the research is exclusively
carried out at the EUI’s premises, the applicable Data protection framework is
the EUI’s Data Protection Policy, complemented when necessary by local privacy
and Data protection laws.

In legal terms, ‘processing of Personal Data’ means: ‘any operation or set of
operations which is performed upon personal data, whether or not by automatic
means, such as collection, recording, organisation, storage, adaptation or
alteration, retrieval, consultation, use, disclosure, transmission,
dissemination or otherwise making available, alignment or combination,
blocking, erasure or destruction’ [11]. Additionally, if a study will use
Personal Data on an individual who can be identified, this may fall under the
remit of the Data Protection Act 2018\. It is the Host Institution’s
responsibility to ensure that the provisions of the Act are met [15].

Article 2 of the EUI’s Data Protection Policy indicates some categories of
data that are more sensitive than other personal data and therefore require
special treatment (‘Sensitive Data’). Sensitive Data are those revealing
racial or ethnic origin, political opinions, religious or philosophical
beliefs, trade-union membership, genetic Data, biometric Data, Data concerning
health and Data relating to sexual orientation or activity. As a rule, the
processing of sensitive data is prohibited. However, Article 8 of the EUI’s
Data Protection Policy provides for specific circumstances, which allow for
the processing of sensitive data. The most common in research is upon the
**Data subject’s _explicit_ consent ** .

As mentioned before, an important area of concern with Social Media Website
(SMW) research is the protection of confidentiality. Similar to other types of
research involving survey or interview Data, protection of participant
identities is critical. Website research may initially be perceived as lower
risk, because participant information can be collected in absence of some
protected information such as address or phone number. Online Data can present
increased risks; studies that publish direct text quotes from an SMW may
directly identify participants. Entering a direct quote from an SMW into a
Google search engine can lead to a specific Web link, such as a link to that
person’s LinkedIn profile, and thus identify the participant.

**SECONDO will not collect or process personal data to conduct its research.
Therefore, a data protection impact assessment shall not be conducted.**

**Nevertheless, the SECONDO consortium and the advisory board will monitor
closely the activities of the project and in case there is a requirement for
collecting/processing personal data a risk evaluation will be conducted.**

The Ethics Board is formed by the following persons, who are closely involved
in ethical procedures within the project and to whom any issue arising during
the project, especially involving end-users would be reported.  
  
<table>  
<tr>  
<th>

**Partner**

</th>  
<th>

**Name**

</th> </tr>  
<tr>  
<td>

UPRC

</td>  
<td>

Christos Xenakis

</td> </tr>  
<tr>  
<td>

SURREY

</td>  
<td>

Emmanouil (Manos) Panaousis

</td> </tr>  
<tr>  
<td>

CUT

</td>  
<td>

Michael Sirivianos

</td> </tr>  
<tr>  
<td>

UBI

</td>  
<td>

Dimirtios Alexandrou

</td> </tr>  
<tr>  
<td>

LST

</td>  
<td>

Evangelos Kotsifakos

</td> </tr>  
<tr>  
<td>

CRO

</td>  
<td>

Nikos Georgopoulos

</td> </tr>  
<tr>  
<td>

FOGUS

</td>  
<td>

Dimitrios Tsolkas

</td> </tr> </table>

The Ethics Board will define a proper procedure for informing the Data
subjects about any ethical related issue (privacy, GDPR compliance etc), its
possible consequences and how their fundamental rights will be safeguarded.
The Ethics Board will make sure that the Data subjects have understood this
information by asking for their consent. These procedures will be kept in a
dedicated git repository that will only be accessible by the Ethics Board and
the SECONDO Platform Administrators. This repository was defined in
deliverable D1.1 - Quality Assurance Plan, while the procedures will be
reported in deliverable D6.2 – Platform Assessment.

As mentioned in D8.1_GEN-requirement_no2, Professor **Konstantinos
Lambrinoudakis** , as a member of the Hellenic Data Protection Authority
(HDPA) he is participating in privacy and GDPR related events, conferences and
talks.

##  5.1 General Data Protection Regulation (GDPR)

If Data controllers intend to use Personal Data that were collected from a
previous research project, they must provide details regarding the initial
Data collection, methodology and informed consent procedure, to the extent
that consent is the appropriate legal basis. They must also confirm that they
comply with the Data protection principles and that they for example have
permission from the Data controller to use the Data in the SECONDO project.

Where the planned use of Data is predicated on the ‘legitimate interests’ of
the Data controller, the nature and purpose of the Dataset must be set out in
detail, together with the safeguards (e.g. anonymisation or pseudonymisation
techniques) that warrant its use in SECONDO project (GDPR , Article 89).

If Data controllers intended Data processing is based on national legislation
or international regulations authorising the research, or a demonstrable
overriding public interest (e.g. public health, social protection) allows to
use a particular Dataset, they must make reference to the relevant Member
State or Union law or policy.

Regarding [16], one of the best ways to mitigate the ethical concerns arising
from the use of Personal Data is to anonymize them so that they no longer
relate to identifiable persons. Data that no longer relate to identifiable
persons, such as aggregate and statistical Data, or Data that have otherwise
been rendered anonymous so that the Data subject cannot be re-identified, are
not Personal Data and are therefore outside the scope of Data protection law.
However, even if the plan is to use only anonymized Datasets, significant
ethics issues may still be raised, and the Database would become rather
unusable. These ethics issues could relate to the origins of the Data or the
manner in which they were obtained. Therefore, the source of the Datasets
intended for use must be specified the and any ethics issues that arise must
be addressed. The potential for misuse of the research methodology or findings
must also be considered, as well as the risk of harm to the group or community
that the Data concern.

Where it is necessary to retain a link between the research subjects and their
Personal Data, Data controllers should, wherever possible, pseudonymize the
Data in order to protect the Data subject’s privacy and minimize the risk to
their fundamental rights in the event of unauthorized access. However, in
SECONDO, because of using only simulated and/or synthetic Data for the
purposes of validation during the project, no pseudonymisation will be used.
Data will be protected by other means of Data security.

When Personal Data moves across borders outside the Union it may put at
increased risk the ability of natural persons to exercise Data protection
rights in particular to protect themselves from the unlawful use or disclosure
of that information.

National authorities in the Member States are being called upon by Union law
to cooperate and exchange Personal Data so as to be able to perform their
duties or carry out tasks on behalf of an authority in another Member State.
cross-border cooperation and agreements to deliver effective Data protection
are essential, particularly if the EU is to maintain its values and uphold its
principles.

To achieve this, the European Data Protection Supervisor (EDPS) regularly
interacts with EU and international Data Protection Authorities (DPAs) and
Regulators to influence and develop cross-border enforcement.

##  5.2 Security and Authentication Legislation

  * **The Directive (EU) 2016/1148 on Network and Information Security (NIS Directive)** , provides legal measures to boost the overall level of cybersecurity in the EU and is the first piece of EUwide cybersecurity legislation. The goal of the NIS Directive is to establish a minimum level of (cyber) security for network and information systems across the EU, particularly for those operating essential services. The Directive addresses specifically operators of essential services and digital service providers. However, it is up to the Member States to assess which entities meet the criteria of the definition of an operator of an essential service. Member States must identify the operators of essential services. 

  

  * **The Regulation on ENISA, the "EU Cybersecurity Agency", and repealing Regulation (EU) 526/2013, and on Information and Communication Technology cybersecurity certification (Cybersecurity Act)** [17] is adopted by the European Parliament on the 12  th  of March 2019. This Act aims to strengthen Europe’s cybersecurity, by replacing existing national cybersecurity certification schemes in European schemes which will define security objectives. For one thing, SECONDO will comply with the Cybersecurity Act’s principles of security by design and by default. 

  

#  6\. The SECONDO FAIR Dataset Template Questionnaire

This section gathers all FAIR forms completed with information from Data
Controllers. The following questionnaires have been addressed by the
responsible partners with a level of detail appropriate to the project’s
progress. The SECONDO FAIR Dataset Template Questionnaire (Table 7-1) includes
a set of questions that all Data Controllers are required to fill in for each
Dataset [3], [7], [8]. The questionnaire template has been reviewed by the
Project Manager, Ethics Board for completeness and compliance with the FAIR
DMP directives.

As mentioned before, the DMP is intended to be a living document in which
information can be made available gradually through successive updates as the
implementation of the project progresses. The Data Controllers will be
responsible to update their respective tables every time significant changes
occur.

**Table 6-1: SECONDO FAIR Dataset Template Questionnaire**  
  
<table>  
<tr>  
<th>

**Project Acronym**

</th>  
<th>

</th>  
<th>

**Project Number**

</th> </tr>  
<tr>  
<td>

**SECONDO**

</td>  
<td>

</td>  
<td>

**823997**

</td> </tr>  
<tr>  
<td>

</td>  
<td>

**Description**

</td> </tr>  
<tr>  
<td>

**Title**

</td>  
<td>

</td>  
<td>

Name of the Dataset

_Please provide a meaningful name so that we can refer to it unambiguously in
the future_

</td> </tr>  
<tr>  
<td>

**Task**

</td>  
<td>

</td>  
<td>

SECONDO task/subtask where

Dataset was generated

_Describe the overall setting of the use case in a scenario style, clarify how
things will really happen during pilots, who will be involved, who will
benefit, etc._

</td> </tr>  
<tr>  
<td>

**Data owner/controller**

</td>  
<td>

</td>  
<td>

Names and addresses of the organizations or people who own/control the Data

</td> </tr>  
<tr>  
<td>

**Time period covered by the Dataset**

</td>  
<td>

</td>  
<td>

Start and end date of the period covered by the Dataset

</td> </tr>  
<tr>  
<td>

**Subject**

</td>  
<td>

</td>  
<td>

Keywords or phrases describing the subjects or content of the Data

</td> </tr>  
<tr>  
<td>

**Language**

</td>  
<td>

</td>  
<td>

All languages used in the Dataset

</td> </tr>  
<tr>  
<td>

**Variable list and codebook**

</td>  
<td>

</td>  
<td>

All variables in the Data files, with description of the variable name,
length, type, values

</td> </tr>  
<tr>  
<td>

**Data quality**

</td>  
<td>

</td>  
<td>

Description of Data quality standards and procedures to assure Data quality

</td> </tr>  
<tr>  
<td>

**File inventory**

</td>  
<td>

</td>  
<td>

All files associated with the project, including extensions

</td> </tr>  
<tr>  
<td>

**File formats**

</td>  
<td>

</td>  
<td>

Format of the file

</td> </tr>  
<tr>  
<td>

**File structure**

</td>  
<td>

</td>  
<td>

Organization of the Data file(s) and layout of the variables, where applicable

</td> </tr>  
<tr>  
<td>

**Necessary software**

</td>  
<td>

</td>  
<td>

Names of any special-purpose software packages required to create, view,
analyse, or otherwise use the Data

</td> </tr>  
<tr>  
<td>

**Details on the procedures for obtaining informed consent**

</td>  
<td>

</td>  
<td>

Please give details on the procedures for obtaining informed consent from the
Data subjects (e.g. providing an information sheet together with the consent
form).

In case of children/minors and/or adults unable to give informed consent,
indicate the tailored methods used to obtain consent. According to the H2020
Guidelines, if the Data subjects are unable to give consent in writing, for
example because of illiteracy, the non-written consent must be formally
documented and independently witnessed. Please explain how you intend to
document oral consent. In the very exceptional case that it can’t be recorded
please give reasons. If you will use deception for another type of Data
subjects, you must obtain retrospective informed and free consent as well as
debrief the participants.

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

Deception requires strong justification and appropriate assessment of the
impact and the risk incurred by both researchers and participants.

</td> </tr>  
<tr>  
<td>

**Measures taken to prevent the risk of enhancing**

**vulnerability/stigmatization of individuals/groups**

</td>  
<td>

</td>  
<td>

_Please indicate any such protective measures (e.g. use of anonymization
techniques, use of pseudonyms, non-disclosure of audio-visual materials, voice
records, etc.)_

</td> </tr>  
<tr>  
<td>

**Description of the processing operations (i.e. what you do with**

**Personal Data and how)**

</td>  
<td>

</td>  
<td>

Processing of ‘Personal Data’ means any operation or set of operations which
is performed upon Personal Data, whether or not by automatic means, such as:

•Collection (digital audio recording, digital video caption, etc.)

•Recording

•Organization and storage

(cloud, LAN or WAN servers)

•Adaptation or alteration

(merging sets, amplification, etc.)

•Retrieval and consultation

•Use

•Disclosure, transmission, dissemination or otherwise making available (share,

exchange, transfer, access to the

Data by a third party)

•Alignment or combination •Blocking, deleting or destruction, etc.

_Please describe in detail the processing operations that you will perform for
conducting your research and give detailed feedback on participants. Indicate
also if a copy of notification/authorization for tracking or observation is
required._

_Any type of research activity may involve processing of Personal Data (ICT
research, genetic sample collection, research activities involving personal
records (financial, criminal, education, etc.), lifestyle and health
information, family histories, physical characteristics, gender and ethnic
background, location tracking and domicile information, etc.)] any method used
for tracking or observing._

</td> </tr> </table>  
<table>  
<tr>  
<th>

1\. Data Summary

</th> </tr>  
<tr>  
<td>

1.1 Purpose

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th> </tr>  
<tr>  
<td>

1.2 Types and formats of Data

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

1.3 Re-use of existing Data

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

1.4 Origin

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

1.5 Expected size

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

1.6 Data utility

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2\. FAIR Data

</td> </tr>  
<tr>  
<td>

2.1 Making Data findable (Dataset description: Metadata, persistent and unique
identifiers e.g.)

</td> </tr>  
<tr>  
<td>

2.1.1 Are the Data produced and/or used in the project discoverable with
Metadata, identifiable and locatable by means of a standard identification
mechanism?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.1.2 What naming conventions do you follow?

</td> </tr>  
<tr>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

2.1.3 Will search keywords be provided that optimize possibilities for re-use?

</th> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.1.4 Do you provide clear version numbers?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.1.5 What Metadata will be created?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2 Making Data openly Accessible

_which Data will be made openly available and if some Datasets remain closed,
the reasons for not giving access; where the Data and associated Metadata,
documentation and code are deposited (repository?); how the Data can be
accessed (are relevant software tools/methods provided)?_

</td> </tr>  
<tr>  
<td>

2.2.1 Which Data produced and/or used in the project will be made openly
available as the default?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.2 How will the Data be made accessible?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.3 What methods or software tools are needed to access the Data?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.4 Is documentation about the software needed to access the Data included?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.5 Is it possible to include the relevant software?

</td> </tr>  
<tr>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

2.2.6 Where will the Data and associated Metadata, documentation and code be
deposited?

</th> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.7 Have you explored appropriate arrangements with the identified
repository?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.8 If there are restrictions on use, how will access be provided?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.9 Is there a need for a Data access committee?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.10 Are there well described conditions for access?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.2.11 How will the identity of the person accessing the Data be ascertained?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**2.3 Making Data Interoperable**

_(which standard or field-specific Data and Metadata vocabularies and methods
will be used)_

</td> </tr>  
<tr>  
<td>

2.3.1 Are the Data produced in the project interoperable?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.3.2 What Data and Metadata vocabularies, standards or methodologies will you
follow to make your Data interoperable?

</td> </tr>  
<tr>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

2.3.3 Will you be using standard vocabularies for all Data types present in
your Data set, to allow interdisciplinary interoperability?

</th> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**2.4 Increase Data re-use**

_(which Data will remain re-usable and for how long, is embargo foreseen; how
the Data is licensed; Data quality assurance procedures)_

</td> </tr>  
<tr>  
<td>

2.4.1 How will the Data be licensed to permit the widest re-use possible?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.4.2 When will the Data be made available for re-use?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.4.3 Are the Data produced and/or used in the project useable by third
parties, in particular after the end of the project?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.4.4 How long is it intended that the Data remains re-usable?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

2.4.5 Are Data quality assurance processes described?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**3 Allocation of resources**

</td> </tr>  
<tr>  
<td>

3.1 What are the costs for making Data FAIR in your project?

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th> </tr>  
<tr>  
<td>

3.2 How will these be covered?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

3.3 Who will be responsible for Data management in your project?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

3.4 Are the resources for long term preservation discussed?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**4 Data security**

</td> </tr>  
<tr>  
<td>

4.1 What provisions are in place for Data security? Please indicate any
methods considered for secure Data storage and transfer of sensitive Data.

_Please indicate any methods considered for Data storage._

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

4.2 Is the Data safely stored in certified repositories for long term
preservation and curation?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**5 Ethical aspects /** Protection of Personal Data notification of processing
operations

</td> </tr>  
<tr>  
<td>

5.1 Are there any ethical or legal issues that can have an impact on Data
sharing?

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

5.2 Is informed consent for Data sharing and long-term preservation included
in questionnaires dealing with Personal Data?

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th> </tr>  
<tr>  
<td>

5.3 Name of the Processor(s)

_Please indicate the names of any other natural or legal person that may
process the Data. If processors can be categorised into groups please refer to
them by groups and not necessarily by name, otherwise indicate their names._

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

5.4 Lawfulness of Processing

_Data Controllers must process only those Personal Data that are necessary
during the project and for a specific purpose. Processing Personal Data that
are not essential to the research may even expose Data Controllers to
allegations of ‘hidden objectives’, i.e. processing information with the Data
subjects’ permission for one purpose and then use that information for another
purpose, without specific permission._

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

5.5 Categories of Data Subjects

_Please indicate the categories of Data subjects involved in the processing
operations of the project._

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

5.6 Categories of Personal Data

_Please list concretely the categories of Personal Data that you will
process:_

  * _Normal Personal Data: name, home address, e-mail address, location Data etc._

  

  * _Sensitive Data: religious beliefs, political opinions, medical Data, sexual identity, etc._

  

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

5.7 Rights of Data subjects

_Regarding Article 16 of the EUI’s Data Protection Policy, Data subjects enjoy
the following rights concerning their Personal Data:_

  * _To be informed whether, how, by whom and for which purpose they are processed_

  

  * _To ask for their rectification, in case they are inaccurate or incomplete_

  

  * _To demand their erasure in case the processing is unlawful or no longer lawful (‘right to be forgotten’)_

  

  * _To block their further processing whilst the conditions under letters b) and c) of this Article are verified._

  

_Note: Please indicate how you will ensure the Data subjects’ rights. E.g.
participants will be free to withdraw at any time without justification. The
Data collected prior to the withdrawal will be deleted. In such a case, you
may need to ensure the erasure of the collected Data while maintaining
anonymity. In order to do so, you may use a pseudonym for each participant
ensuring that the key to the pseudonyms is passwordprotected and available
only to the Data Controller._

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5.8 Safeguards taken to protect the Data subjects’ identity.

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_Regarding Article 2 of the EUI’s DP Policy, Identifiable persons can be
identified directly or indirectly, in particular by reference to an
identification number or to one or more factors specific to their physical,
physiological, genetic, mental, economic, cultural or social identity. Please
provide details on the measures taken to avoid direct or indirect
identification of the Data subjects, e.g. by using anonymisation techniques or
pseudonyms. E.g. names of the Data subjects will not be disclosed, at any
time, in audio recording and published material Pseudonyms (a reversible
system of coding in order to be able to re-contact participants if needed)
will be used in all documentation, and any additional information that may
reveal the identity of participants will be concealed when publishing._

_Destroy any residual information that could lead to the identification of
participants at the end of the project. You must explain this procedure
clearly to participants during the ‘recruitment’ process._

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6\. Other issues

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6.1 Do you make use of other national/funder/sectorial/departmental procedures
for Data management? If yes, which ones?

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