Opendata, web and dolomites


Quantifying and Protecting the Privacy of Genomic Data

Total Cost €


EC-Contrib. €






Project "GenoPri" data sheet

The following table provides information about the project.


Organization address
postcode: 6800

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 Turkey [TR]
 Project website
 Total cost 157˙845 €
 EC max contribution 157˙845 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-05-01   to  2018-07-28


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Genomic data carries a lot of sensitive information about its owner such as his predispositions to sensitive diseases, ancestors, physical attributes, and genomic data of his relatives (leading to interdependent privacy risks). Individuals share vast amount of information on the Web, and some of this information can be used to infer their genomic data. Hence, there is a need to clearly understand the privacy risks on genomic data of individuals considering publicly available information on the Web. It is also crucial to protect genomic privacy of individuals without compromising the utilization of genomic data in research and healthcare.

The two main objectives of this project are (i) to develop a new unifying framework for quantification of genomic privacy of individuals and (ii) to establish a complete framework for privacy-preserving utilization, sharing, and verification of genomic data under real-life threat models. Graph-based, iterative algorithms previously developed by the applicant to efficiently analyze big data and make inference from it will be the foundation for the new quantification framework. To achieve the holistic genomic privacy objective, cryptographic tools, techniques from information theory, and statistics (differential privacy) will be used.

This project will be a significant step towards understanding the privacy risks on genomic data of individuals and protecting the privacy of genomic data. It will also provide a new vision for security and privacy of health-related data in general and will find many implications in other domains such as banking and online social networks. The results of the project will also have an impact on future policies and legislation about protection of health-related data.

This EF will have big impact on the future career of the applicant by helping him build new connections, enhance his expertise, increase his visibility in the field of security and privacy, and improve his independent research skills.


year authors and title journal last update
List of publications.
2018 Nora von Thenen, Erman Ayday, A Ercument Cicek
Re-identification of individuals in genomic data-sharing beacons via allele inference
published pages: , ISSN: 1367-4803, DOI: 10.1093/bioinformatics/bty643
Bioinformatics 2019-06-13
2017 Mathias Humbert, Erman Ayday, Jean-Pierre Hubaux, Amalio Telenti
Quantifying Interdependent Risks in Genomic Privacy
published pages: 1-31, ISSN: 2471-2566, DOI: 10.1145/3035538
ACM Transactions on Privacy and Security 20/1 2019-06-13
2017 Nora von Thenen, Erman Ayday, Ercument Cicek
Inference Attacks Against Genomic Data-Sharing Beacons
published pages: , ISSN: , DOI:
2017 Erman Ayday, Mathias Humbert
Inference Attacks against Kin Genomic Privacy
published pages: 29-37, ISSN: 1540-7993, DOI: 10.1109/MSP.2017.3681052
IEEE Security & Privacy 15/5 2019-06-13
2016 Erman Ayday
Cryptographic Solutions for Genomic Privacy
published pages: 328-341, ISSN: , DOI: 10.1007/978-3-662-53357-4_22
Financial Cryptography and Data Security 2019-06-13
2017 Anisa Halimi, Erman Ayday
Profile Matching Across Unstructured Online Social Networks: Threats and Countermeasures
published pages: , ISSN: , DOI:
2016 Volkan Kucuk, Erman Ayday
Profile Matching Across Unstructured Online Social Networks
published pages: , ISSN: , DOI:
2018 Iman Deznabi, Mohammad Mobayen, Nazanin Jafari, Oznur Tastan, Erman Ayday
An Inference Attack on Genomic Data Using Kinship, Complex Correlations, and Phenotype Information
published pages: 1333-1343, ISSN: 1545-5963, DOI: 10.1109/TCBB.2017.2709740
IEEE/ACM Transactions on Computational Biology and Bioinformatics 15/4 2019-06-13
2016 Zhicong Huang, Erman Ayday, Huang Lin, Raeka S. Aiyar, Adam Molyneaux, Zhenyu Xu, Jacques Fellay, Lars M. Steinmetz, Jean-Pierre Hubaux
A privacy-preserving solution for compressed storage and selective retrieval of genomic data
published pages: 1687-1696, ISSN: 1088-9051, DOI: 10.1101/gr.206870.116
Genome Research 26/12 2019-06-13
2017 Gulce Kale, Erman Ayday, Oznur Tastan
A utility maximizing and privacy preserving approach for protecting kinship in genomic databases
published pages: 181-189, ISSN: 1367-4803, DOI: 10.1093/bioinformatics/btx568
Bioinformatics 34/2 2019-06-13
2017 Erman Ayday, Qiang Tang, Arif Yilmaz
Cryptographic Solutions for Credibility and Liability Issues of Genomic Data
published pages: 1-1, ISSN: 1545-5971, DOI: 10.1109/TDSC.2017.2690422
IEEE Transactions on Dependable and Secure Computing 2019-06-13
2017 Arif Yilmaz, Erman Ayday
Collusion-Secure Watermarking for Sequential Data
published pages: , ISSN: , DOI:

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

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