Youssoupha GAYE

Affiliated project: SEC-FEDGEN

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

Thesis supervisor : Maïssa MBAYE
maissa.mbaye@ugb.edu.sn

Co-director : Joke A.Badejo
joke.badejo@covenantuniversity.edu.ng

Other supervisor or contributor
Dame DIONGUE
dame.diongue@ugb.edu.sn

Start date: 01/09/21
Anticipated date of thesis defense: End of 2024 or beginning of 2025
ORCID profile: 0009-0003-6101-9995

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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.