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PlAtform for PrivAcY preserving data Analytics


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.

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.

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.

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.

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.

Public Project Website

This deliverable consists of the project’s website.

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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, Issue 11025, 2018, Page(s) 389-404
DOI: 10.1007/978-3-030-00305-0_27

Private neural network predictions

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

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, Issue July 15th 2019, 2019

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, Issue 118, 2019

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

PAC: Privacy-preserving Arrhythmia Classification with neural networks

Author(s): Mohamad Mansouri, Beyza Bozdemir, Melek Önen, Orhan Ermis
Published in: 12th International Symposium on Foundations and Practice of Security (FPS 2019), Issue 12056, 2019