Oana Stan · EDBT 2026 Demo

EDBT 2026 Demo / reviewers in the wild / expert

Oana Stan

dblp:139/7654 · DBLP profile ↗

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10ranked-venue papers

1as first author

6since 2021 · last 2026

0000-0003-2419-9037ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 7 · 1 first-author · 5 since 2021Computer networks · 1

Year	Publication	Venue	Position
2026	MOSAIC-FL, a Micro-Service Based Privacy-Preserving Framework with Application to Genomics Paul Largillier, Karl Paygambar, Cédric Gouy-Pailler, Vincent Meyer, Mallek Mziou, Oana Stan	SECRYPT (1)	6
2023	Combining homomorphic encryption and differential privacy in federated learning abstract Recent works have investigated the relevance and practicality of using techniques such as Differential Privacy (DP) or Homomorphic Encryption (HE) to strengthen training data privacy in the context of Federated Learning protocols. As these two techniques cover different sources of confidentiality threats (other clients/end-users for the former, aggregation server for the latter), there is a need to consistently combine them in order to bridge the gap towards more realistic deployment scenarios. In this paper, we achieve that goal by means of a novel stochastic quantization operator which allows us to establish DP guarantees when the noise is both quantized and bounded due to the use of HE. The paper is concluded by experiments on the FEMNIST dataset which show that the precision required to get state-of-the art privacy/utility trade-off (which directly impacts HE parameters and, hence, HE operations performances) results in a computation time overhead between 0.2% and 1.1% imputable to HE (depending on the key setup, either single key or threshold), for the whole training of a 500k parameters model and state-of-the-art privacy/utility trade-off. Arnaud Grivet Sébert, Marina Checri, Oana Stan, Renaud Sirdey, Cédric Gouy-Pailler	PST	3
2022	SecTL: Secure and Verifiable Transfer Learning-based inference abstract International audience Abbass Madi, Oana Stan, Renaud Sirdey, Cédric Gouy-Pailler	ICISSP	2
2022	A Secure Federated Learning: Analysis of Different Cryptographic Tools abstract International audience Oana Stan, Vincent Thouvenot, Aymen Boudguiga, Katarzyna Kapusta, Martin Zuber, Renaud Sirdey	SECRYPT	1
2021	Privacy Preserving Services for Intelligent Transportation Systems with Homomorphic Encryption abstract International audience Aymen Boudguiga, Oana Stan, Abdessamad Fazzat, Houda Labiod, Pierre-Emmanuel Clet	ICISSP	2
2021	RandSolomon: Optimally Resilient Random Number Generator with Deterministic Termination abstract International audience Luciano Freitas de Souza, Andrei Tonkikh, Sara Tucci Piergiovanni, Renaud Sirdey, Oana Stan, Nicolas Quero, Petr Kuznetsov	OPODIS	5
2020	Homomorphic Encryption at Work for Private Analysis of Security Logs abstract International audience Aymen Boudguiga, Oana Stan, Hichem Sedjelmaci, Sergiu Carpov	ICISSP	2
2018	Efficient Evaluation of Low Degree Multivariate Polynomials in Ring-LWE Homomorphic Encryption Schemes Sergiu Carpov, Oana Stan	ISPEC	2
2018	An OpenNCP-based Solution for Secure eHealth Data Exchange Mariacarla Staffa, Luigi Sgaglione, Giovanni Mazzeo, Luigi Coppolino, Salvatore D'Antonio, Luigi Romano, Erol Gelenbe, Oana Stan, Sergiu Carpov, Evangelos Grivas, Paolo Campegiani, Luigi Castaldo, Konstantinos Votis, Vassilis Koutkias, Ioannis Komnios	J. Netw. Comput. Appl.	8
2016	An Architecture for Practical Confidentiality-Strengthened Face Authentication Embedding Homomorphic Cryptography abstract In this paper, we propose and experiment a system architecture which intends to significantly strengthen the security of biometric authentication with respect to the confidentiality(-by-design) of the users' references needed to perform such a function. Our architecture has been designed to ensure that these biometric references are permanently encrypted and that the (single) server processing them has no decryption capability (in particular, does not have access to any decryption key). In order to do so, we use homomorphic encryption techniques which allow to perform calculations directly over encrypted data. We report on the careful architectural choices and agressive optimizations we had to make in order to be able to deploy an off-the-shelf face recognition module into this architecture. As the performance results presented in the paper demonstrate, we claim to have achieved practically relevant levels of performance and security in a realistic setting. Nabil Bouzerna, Renaud Sirdey, Oana Stan, Philippe Wolf	CloudCom	3