DSTN Scholars

DSTN > Scholars > Youssoupha GAYE

Affiliated project: SEC-FEDGEN

Affiliated CEA : Gaston Berger University
CApIC-ACE/Covenant University

Thesis supervisor : Maïssa MBAYE

Co-director : Joke A.Badejo

Other supervisor or contributor

DSTN > Scholars > Youssoupha GAYE

Anticipated date of thesis defense :
Late 2024 or early 2025
ORCID profile
: 0009-0003-6101-9995

Project title: Contributions on data security and privacy models for a federated cloud.

Scientific project summary :
This PhD focuses on the development of a security framework called SEC-FEDGEN for the federated genomics cloud computing infrastructure in Africa. This framework aims to address two main challenges: protecting the infrastructure from malicious behavior and ensuring confidentiality when using sensitive genomic data across borders. Given the scarcity of network connectivity in Africa, securing federated clouds becomes particularly challenging. Existing literature proposes various approaches such as SDN-based delegation models, game-theoretic models and protocol-based solutions, but these approaches may not adequately address the unique requirements of SEC-FEDGEN. Key scientific challenges include data protection, anomaly detection, privacy violations and security level negotiation. The aim is to design a framework that enables anomaly detection and privacy protection while ensuring compliance with national data protection legislation, thus facilitating the secure sharing and analysis of sensitive genomic data in Africa.

Summary of results:
Anonymization is the traditional approach to preserving privacy, aimed at masking the link between the quasi-identifier and sensitive data. However, there is no formal measure of the quality of the anonymization process in terms of its ability to prevent re-identification. We have examined the issue of assessing anonymization quality and introduced a new metric, Mmaq (Metric to Measure Anonymization Quality), for this purpose. It can be used to evaluate the anonymization of one or more attributes. The metric is a combination of the Shannon index, which measures diversity, and a stabilization factor, which corrects the Shannon index for pathological cases. Initial results suggest that Mmaq can be used to classify attributes as identifiers, quasi-identifiers and anonymizers. In addition, it can be used as a checker of anonymization compliance with cloud computing privacy policy.

Objectives / Expected results : In this work, the main objective is to design an anomaly detection and privacy protection framework for a federated cloud architecture. 

Outlook after completion of thesis
Upon completion of the thesis, I anticipate the deployment and adoption of the security framework developed within FEDGEN, marking an important milestone in safeguarding genomic data and promoting research initiatives across Africa. The implementation of the framework will provide local research communities with the tools they need to conduct genomic studies safely, thereby strengthening scientific capacity and impacting society through better healthcare solutions and advances in genomic research.
Armed with this experience and newly acquired knowledge, I'd be keen to seize new opportunities in the field of informatics, whether to advance research, teach or contribute to innovative projects.

Scientific publications


Contribution / added value to the affiliated project :

  1. State of the art on security and privacy in federated cloud computing. 
  2. Propose a data security model adapted to the federated distributed cloud (confidentiality, integrity monitoring, distributed anomaly detection, etc.).
  3. Propose a model for protecting private data in a federated cloud with cloudlets located in different jurisdictions (interoperability, verification of anonymization, access model, compliance, etc.).
  4. Build a federated cloud based on a public (Huawei, AWS and Google) and private (FEDGEN, Small Grid) cloud service.
  5. Create a federated cloud based on public cloud providers. 
  6. Implement the framework and models proposed on the FEDGEN test bench.

Supervisor : Pr. Maïssa MBAYE, CEA-MITIC

Co-supervisor : Dr. Joke BADEJO, CapIC-ACE

Youssoupha Gaye, Maissa Mbaye, Dame Diongue, Ousmane Dieng, Emmanuel Adetiba, and Joke A. Badejo "Federated Clouds: A New Metric for Measuring the Quality of Data Anonymization" in Ubiquitous Networking, Lecture Notes in Computer Science, 9th International Symposium, UNet 2023 (accepted for publication in 2024) in Springer LNCS

Other contributors to doctoral supervision :

Prof. Ezekiel ADEBIYI, CapIC-ACE

Prof. Emmanuel ADETIBA, CApIC-ACE