Annonce en lien avec l’Action/le Réseau : aucun
Laboratoire/Entreprise : Laboratoire d’Informatique Fondamentale d’Orléans – INSA Centre Val de Loire
Durée : 36 mois
Contact : cedric.eichler@insa-cvl.fr
Date limite de publication : 2019-05-30
Contexte :
The PhD is funded by the ANR (french national research agency) in the context of the national SENDUP project. PhD will be co-supervised with the Laboratoire d’Informatique de Grenoble (LIG) and will start on September 1, 2019 (may be postponed upon candidates request until December).
Sujet :
**Scientific Context**
The amount of data produced by individuals and corporations has dramatically increased during the last decades. This generalized gathering of data brings opportunities (e.g., building new knowledge using this ”Big Data”) but also new privacy challenges.
The general public express a growing distrust over personal data exploitation, which has been met with successive strengthened regulations (e.g. EU general data protection regulation).
This has led to a growing interest for data sanitization, the art of disclosing personal data without jeopardizing privacy, and data-set anonymisation. An anonymized dataset is a dataset which is difficult, costly, or impossible to relate to real individuals. Both domains aim to maintain a certain data quality while ensuring privacy in order to produce information as useful as possible.
The LIFO (Orléans/Bourges) and LIG (Grenoble) laboratories are working on an innovative ANR project for efficient sanitization and anonymization for data stored as graphs with an underlying semantic (e.g., RDF).
**Objectives**
The PhD applicant will integrate and collaborate with SENDUP’s team. Its main objectives will be:
-introduce new knowledge- and usage-based utility metrics for graph data-bases.
-introduce new privacy guaranties and metrics (e.g. k-anonymity, differential privacy) for graph data-bases.
-contribute to the suite of software modules implementing the proposed algorithms
**Some relevant references**
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith. ”Analyzing graphs with node differential privacy”. Proceedings of the 10th Theory of Cryptography Conference, TCC 2013
A. A. Mubark, E. Elabd, and H. Abdulkader. ”Semantic anonymization in publishing categorical sensitive attributes”. Proceedings of the 8th International Conference on Knowledge and Smart Technology, 2016
Remy Delanaux, Angela Bonifati, Marie-Christine Rousset, and Romuald Thion. ”Query-Based Linked Data Anonymization”. Proceedings of the International Semantic Web Conference, 2018.
Shouling Ji, Weiqing Li, Prateek Mittal, Xin Hu, and Raheem Beyah. “SecGraph: a uniform and open-source evaluation system for graph data anonymization and de-anonymization”. Proceedings of the 24th USENIX Conference on Security Symposium (SEC’15), 2015.
World Wide Web Consortium, RDF https://www.w3.org/RDF/
Profil du candidat :
Applicants should be fluent in written and spoken english, have good coding skills, and be interested in privacy problematics
Formation et compétences requises :
Master degree in computer science or equivalent. A specialization in database, knowledge management or security/privacy is a plus.
Adresse d’emploi :
INSA Centre Val de Loire, 88 boulevard Lahitolle 18022 Bourges
Document attaché : theseSendUp.pdf