Opendata, web and dolomites


PlAtform for PrivAcY preserving data Analytics

Total Cost €


EC-Contrib. €






Project "PAPAYA" data sheet

The following table provides information about the project.


Organization address
city: BIOT
postcode: 6410

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Project website
 Total cost 3˙763˙130 €
 EC max contribution 2˙949˙417 € (78%)
 Programme 1. H2020-EU.3.7.6. (Ensure privacy and freedom, including in the Internet and enhance the societal, legal and ethical understanding of all areas of security, risk and management)
 Code Call H2020-DS-SC7-2017
 Funding Scheme IA
 Starting year 2018
 Duration (year-month-day) from 2018-05-01   to  2021-04-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EURECOM FR (BIOT) coordinator 558˙255.00
3    KARLSTADS UNIVERSITET SE (KARLSTAD) participant 492˙500.00
4    ORANGE SA FR (PARIS) participant 432˙075.00
5    MEDIACLINICS ITALIA SRL IT (TRENTO TN) participant 392˙437.00
6    ATOS SPAIN SA ES (MADRID) participant 360˙937.00


 Project objective

The valuable insights that can be inferred from analytics of data generated and collected from a variety of devices and applications are transforming businesses and are therefore one of the key motivations for organisations to adopt such technologies. Nevertheless, the data being analysed and processed are highly sensitive and put the individuals’ rights to privacy at risk. With the imminent arrival of the European General Data Protection Regulation (GDPR), companies are coerced to adopt privacy enhancing technologies that, on the one hand, protect data to ensure their clients’ privacy and on the other hand, allow their processing while keeping them meaningful, useful, and protected at the same time. The PAPAYA project aims at addressing the privacy concerns when data analytics tasks are performed by untrusted third-party data processors. Since these tasks may be performed obliviously on protected data (i.e. encrypted data), the PAPAYA will design and develop dedicated privacy preserving data analytics primitives that will enable data owners to extract valuable information from this protected data, while being cost-effective and accurate. The PAPAYA project will consider compliance with the GDPR as a key enabler to provide solutions that minimize the privacy risks while increasing trust in third-party data processors by means of auditing and visualization modules (a dashboard). The PAPAYA primitives as well as the dashboard will be combined in an integrated platform that will be designed, implemented and validated through a set of use cases reflecting relevant real world applications (namely, healthcare analytics and web & mobile data analytics).


List of deliverables.
Public Project Website Websites, patent fillings, videos etc. 2020-03-25 17:17:28
Functional Design and Platform Architecture Documents, reports 2020-03-25 17:17:28
Requirements specification Documents, reports 2020-03-25 17:17:28
Preliminary Design of Privacy preserving Data Analytics Documents, reports 2020-03-25 17:17:28
Dissemination and Communication plan Documents, reports 2020-03-25 17:17:28
First Project Progress Report Documents, reports 2020-03-25 17:17:28
Risk Management Artefacts for Increased Transparency Documents, reports 2020-03-25 17:17:28
Innovation Strategy and Plan Documents, reports 2020-03-25 17:17:28
Use case specification Documents, reports 2020-03-25 17:17:28

Take a look to the deliverables list in detail:  detailed list of PAPAYA deliverables.


year authors and title journal last update
List of publications.
2019 Eleonora Ciceri, Marco Mosconi, Melek Önen, Orhan Ermis
PAPAYA: A PlAtform for PrivAcY preserving data Analytics
published pages: , ISSN: , DOI:
ERCIM News. Special Theme: Digital Health 118 2020-03-25
2019 Gamze Tillem, Beyza Bozdemir, Melek Önen
Private neural network predictions
published pages: , ISSN: , DOI:
ICT.OPEN2019, Dutch Digital Conference 2020-03-25
2019 Mohamad Mansouri, Beyza Bozdemir, Melek Önen, Orhan Ermis
PAC: Privacy-preserving Arrhythmia Classification with neural networks
published pages: , ISSN: , DOI:
12th International Symposium on Foundations and Practice of Security (FPS 2019) 12056 2020-03-25
2019 Gamze Tillem, Beyza Bozdemir, Melek Önen, Orhan Ermis
Privacy preserving neural network classification: A hybrid solution
published pages: , ISSN: , DOI:
PUT 2019, Open Day for Privacy, Usability, and Transparency, Co-located with the 19th Privacy Enhancing Technologies Symposium July 15th 2019 2020-03-25
2019 Tjerk Timan,Zoltan Mann, Rosa Araujo,Alberto Crespo Garcia,Ariel Farkash,Antoine Garnier,Akrivi Vivian Kiousi,Paul Koster,Antonio Kung, Giovanni Livraga, Roberto Díaz Morales, Melek Önen,Ángel Palomares, Angel Navia Vázquez,Andreas Metzger
Data protection in the era of artificial intelligence. Trends, existing solutions and recommendations for privacy-preserving technologies
published pages: , ISSN: , DOI:

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The information about "PAPAYA" are provided by the European Opendata Portal: CORDIS opendata.

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