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PlAtform for PrivAcY preserving data Analytics

Deliverables

Requirements specification

This deliverable will summarize the analysis of relevant concepts, processes and their relationships and will define requirements for the platform for privacy preserving analytics that will be developed in WP4.

Preliminary Design of Privacy preserving Data Analytics

This deliverable will introduce the preliminary design and specification of the different privacy preserving modules for data analytics proposed in Tasks 3.1 and 3.2.

Transparent Privacy preserving Data Analytics

This deliverable will describe the construction of the completed PET and its visualizations.

PAPAYA Platform Guide

A guide for platform operators on operating the PAPAYA platform.

First Project Progress Report

This document will include the record of activities related to technical progress, dissemination and exploitation taken during the first year of the project. It will describe the use of resources (financial and personnel) at the end of the first year. Any deviations from the plan will be described together with the foreseen consequences and some proposals for any necessary re-planning.

Intermediate Dissemination and Communication Report

This deliverable will describe dissemination and communication activities that have been performed during the first part of the project.

Second Project Progress Report

Similar to D1.3, this deliverable will provide a report on second year achievements including innovation activities.

Use case specification

This deliverable will describe the detailed use case specifications for all the PAPAYA scenarios

Dissemination and Communication plan

This deliverable will present dissemination and communication activities that are planned.

Complete Specification and Implementation of Privacy preserving Data Analytics

This deliverable will provide a complete description of the design and development of the modules for privacy preserving data analytics introduced in D3.1.

Progress report on platform implementation and PETs integration

This deliverable will describe the intermediate platform implementation and PETs integration, including intermediate implementation of the dashboard.

Refinement Recommendations for the Platform

This deliverable will report lessons learned from validation and recommendations on technology refinement.

Functional Design and Platform Architecture

This deliverable will summarize analysis of the state of the art selected technologies and will present the platform functional design and architecture, including design of the dashboard.

Final Dissemination and Communication Report

This final deliverable will present dissemination and communication activities that have been performed during the whole project.

Innovation Strategy and Plan

This document establishes the strategy, processes, milestones and role assignments to ensure an innovation-driven development work. The document will also include an early market and technologies assessment to serve as input to the work packages action plan.

Interim Resource Reporting -Second Reporting Period

This deliverable will provide a report on the use of resources from September 2019 until November 2020, due to the extension of the project

Final Project Progress Report

Similarly to D1.3 and D1.4, this deliverable will provide a report on project activities including innovation activities taken during the final year.

Risk Management Artefacts for Increased Transparency

This document will provide a survey of relevant risk management methods, focusing on artefacts that can increase transparency towards data subjects for the privacy-utility trade-off, and the design of the PET.

Final report on platform implementation and PETs integration

This deliverable will describe the final platform implementation and PETs integration after validation on project use cases, including final version of the dashboard

E-health use case validation

This deliverable will define the testing strategy and the validation criteria. The pilot will test the technologies underlying the PAPAYA platform (WP3 and WP4). This test will be realised according to the e-health use case specification from T2.1 and validated according to these criteria. This deliverable will report the validation results.

Web analytics use case validation

Similar to D5.1, this deliverable will report the validation results according to the web analytics use case specification from T2.1.

Public Project Website

This deliverable consists of the project’s website.

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Publications

New data analytics platform eases privacy concerns for owners

Author(s): M. Önen
Published in: 2021
Publisher: CORDIS

Private neural network predictions

Author(s): Gamze Tillem, Beyza Bozdemir, Melek Önen
Published in: ICT.OPEN2019, Dutch Digital Conference, 2019
Publisher: ICT.OPEN

Privacy preserving neural network classification: A hybrid solution

Author(s): Gamze Tillem, Beyza Bozdemir, Melek Önen, Orhan Ermis
Published in: PUT 2019, Open Day for Privacy, Usability, and Transparency, Co-located with the 19th Privacy Enhancing Technologies Symposium, July 15th 2019, 2019
Publisher: PUT

PAPAYA: A PlAtform for PrivAcY preserving data Analytics

Author(s): Eleonora Ciceri, Marco Mosconi, Melek Önen, Orhan Ermis
Published in: ERCIM News. Special Theme: Digital Health, 118, 2019
Publisher: ERCIM

Data protection in the era of artificial intelligence. Trends, existing solutions and recommendations for privacy-preserving technologies

Author(s): 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
Published in: 2019
Publisher: BDVA

Machine Learning–Based Analysis of Encrypted Medical Data in the Cloud: Qualitative Study of Expert Stakeholders’ Perspectives

Author(s): A. S. Alaqra, B. Kane, S. Fischer-Hübner
Published in: Journal of Medical Internet Research - JMIR Human Factors, 2021
Publisher: JMIR Publications

WeStat: a Privacy-Preserving Mobile Data Usage Statistics System

Author(s): Sébastien Canard, Nicolas Desmoulins, Sébastien Hallay, Adel Hamdi, Dominique Le Hello
Published in: Proceedings of the 2021 ACM Workshop on Security and Privacy Analytics, 2021, Page(s) 5-14, ISBN 9781450383202
Publisher: ACM
DOI: 10.1145/3445970.3451151

SwaNN: Switching among Cryptographic Tools for Privacy-preserving Neural Network Predictions

Author(s): Gamze Tillem, Beyza Bozdemir, Melek Önen
Published in: Proceedings of the 17th International Joint Conference on e-Business and Telecommunications, 2020, Page(s) 497-504, ISBN 978-989-758-446-6
Publisher: SCITEPRESS - Science and Technology Publications
DOI: 10.5220/0009890704970504

Privacy-preserving Density-based Clustering

Author(s): Beyza Bozdemir, Sébastien Canard, Orhan Ermis, Helen Möllering, Melek Önen, Thomas Schneider
Published in: Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security, 2021, Page(s) 658-671, ISBN 9781450382878
Publisher: ACM
DOI: 10.1145/3433210.3453104

Wearable Devices and Measurement Data: An Empirical Study on eHealth and Data Sharing

Author(s): Ala Sarah Alaqra, Bridget Kane
Published in: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, Page(s) 443-448, ISBN 978-1-7281-9429-5
Publisher: IEEE
DOI: 10.1109/cbms49503.2020.00090

Using PAPAYA for eHealth - Use Case Analysis and Requirements

Author(s): Ala Sarah Alaqra, Eleneora Ciceri, Simone Fischer-Hubner, Bridget Kane, Marco Mosconi, Sauro Vicini
Published in: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, Page(s) 437-442, ISBN 978-1-7281-9429-5
Publisher: IEEE
DOI: 10.1109/cbms49503.2020.00089

ProteiNN: Privacy-preserving One-to-Many Neural Network Classifications

Author(s): Beyza Bozdemir, Orhan Ermis, Melek Önen
Published in: Proceedings of the 17th International Joint Conference on e-Business and Telecommunications, 2020, Page(s) 397-404, ISBN 978-989-758-446-6
Publisher: SCITEPRESS - Science and Technology Publications
DOI: 10.5220/0009829603970404

SoK: Cryptography for Neural Networks

Author(s): Monir Azraoui, Muhammad Bahram, Beyza Bozdemir, Sébastien Canard, Eleonora Ciceri, Orhan Ermis, Ramy Masalha, Marco Mosconi, Melek Önen, Marie Paindavoine, Boris Rozenberg, Bastien Vialla, Sauro Vicini
Published in: Privacy and Identity Management. Data for Better Living: AI and Privacy - 14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School, Windisch, Switzerland, August 19–23, 2019, Revised Selected Papers, 576, 2020, Page(s) 63-81, ISBN 978-3-030-42503-6
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-42504-3_5

Blind Functional Encryption

Author(s): Sébastien Canard, Adel Hamdi, Fabien Laguillaumie
Published in: Information and Communications Security - 22nd International Conference, ICICS 2020, Copenhagen, Denmark, August 24–26, 2020, Proceedings, 12282, 2020, Page(s) 183-201, ISBN 978-3-030-61077-7
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-61078-4_11

Vision: A Noisy Picture or a Picker Wheel to Spin? Exploring Suitable Metaphors for Differentially Private Data Analyses

Author(s): Farzaneh Karegar, Simone Fischer-Hübner
Published in: EuroUSEC, 2021
Publisher: ACM

A collaborative training approach for stress detection

Author(s): Eleonora Ciceri, Marco Mosconi, Boris Rozenberg, Ron Shmelkin
Published in: CIBB 2021 Computational Intelligence Methods for Bioinformatics and Biostatistics, 2021
Publisher: Springer

Interactive Focus Group GDPR-compliant Dynamic Consent Management

Author(s): E. Schlehahn, S. Fischer-Hübner, R. Wenning, M. Ptarick, F. Karegar
Published in: IFIP Summer School on Identity Management, 2019
Publisher: Springer

From Design Requirements to Effective Privacy Notifications: Empowering Users of Online Services to Make Informed Decisions

Author(s): Patrick Murmann, Farzaneh Karegar
Published in: International Journal of Human–Computer Interaction, 2021, Page(s) 1-26, ISSN 1044-7318
Publisher: Lawrence Erlbaum Associates Inc.
DOI: 10.1080/10447318.2021.1913859

Protecting Citizens’ Personal Data and Privacy: Joint Effort from GDPR EU Cluster Research Projects

Author(s): Renata M. de Carvalho, Camillo Del Prete, Yod Samuel Martin, Rosa M. Araujo Rivero, Melek Önen, Francesco Paolo Schiavo, Ángel Cuevas Rumín, Haralambos Mouratidis, Juan C. Yelmo, Maria N. Koukovini
Published in: SN Computer Science, 1/4, 2020, ISSN 2662-995X
Publisher: Springer
DOI: 10.1007/s42979-020-00218-8

Traiter des données multimédia chiffrées grâce au chiffrement homomorphe

Author(s): S. Canard, S. Carpov, C. Fontaine, R. Sirdey
Published in: Sécurité multimédia 2 : biométrie, protection et chiffrement multimédia, 2021, Page(s) 191-232
Publisher: ISTE

FHE-Compatible Batch Normalization for Privacy Preserving Deep Learning

Author(s): Alberto Ibarrondo, Melek Önen
Published in: Data Privacy Management, Cryptocurrencies and Blockchain Technology - ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings, 11025, 2018, Page(s) 389-404, ISBN 978-3-030-00304-3
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-00305-0_27

PAC: Privacy-Preserving Arrhythmia Classification with Neural Networks

Author(s): Mohamad Mansouri, Beyza Bozdemir, Melek Önen, Orhan Ermis
Published in: Foundations and Practice of Security - 12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers, 12056, 2020, Page(s) 3-19, ISBN 978-3-030-45370-1
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-45371-8_1