Maryline Laurent

Montassar Naghmouchi, Maryline Laurent, Claire Levallois-Barth et al.

National digital identity systems have become a key requirement for easy access to online public services, specially during Covid-19. While many countries have adopted a national digital identity system, many are still in the process of establishing one. Through a comparative analysis of the technological and legal dimensions of a few selected national digital identity solutions currently being used in different countries, we highlight the diversity of technologies and architectures and the key role of the legal framework of a given digital identity solution. We also present several key issues related to the implementation of these solutions, how to ensure the State sovereignty over them, and how to strike the right balance between private sector and public sector needs. This position paper aims to help policy makers, software developers and concerned users understand the challenges of designing, implementing and using a national digital identity management system and establishing a legal framework for digital identity management, including personal data protection measures. The authors of this paper have a favorable position for self-sovereign identity management systems that are based on Blockchain technology, and we believe they are the most suitable for national digital identity systems.

Faiz Taleb, Ivan Gazeau, Maryline Laurent

Generative models can unintentionally memorize training data, posing significant privacy risks. This paper addresses the memorization phenomenon in time series imputation models, introducing the Loss-Based with Reference Model (LBRM) algorithm. The LBRM method leverages a reference model to enhance the accuracy of membership inference attacks, distinguishing between training and test data. Our contributions are twofold: first, we propose an innovative method to effectively extract and identify memorized training data, significantly improving detection accuracy. On average, without fine-tuning, the AUROC improved by approximately 40\%. With fine-tuning, the AUROC increased by approximately 60\%. Second, we validate our approach through membership inference attacks on two types of architectures designed for time series imputation, demonstrating the robustness and versatility of the LBRM approach in different contexts. These results highlight the significant enhancement in detection accuracy provided by the LBRM approach, addressing privacy risks in time series imputation models.

Montassar Naghmouchi, Hella Kaffel, Maryline Laurent

Nowadays, open standards for self-sovereign identity and access management enable portable solutions that are following the requirements of IoT systems. This paper proposes a blockchain-based identity and access management system for IoT -- specifically smart vehicles -- as an example of use-case, showing two interoperable blockchains, Ethereum and Hyperledger Indy, and a self-sovereign identity model.

Seryne Rahali, Maryline Laurent, Souha Masmoudi et al.

This paper proposes a new recommendation system preserving both privacy and utility. It relies on the local differential privacy (LDP) for the browsing user to transmit his noisy preference profile, as perturbed Bloom filters, to the service provider. The originality of the approach is multifold. First, as far as we know, the approach is the first one including at the user side two perturbation rounds - PRR (Permanent Randomized Response) and IRR (Instantaneous Randomized Response) - over a complete user profile. Second, a full validation experimentation chain is set up, with a machine learning decoding algorithm based on neural network or XGBoost for decoding the perturbed Bloom filters and the clustering Kmeans tool for clustering users. Third, extensive experiments show that our method achieves good utility-privacy trade-off, i.e. a 90$\%$ clustering success rate, resp. 80.3$\%$ for a value of LDP $ε= 0.8$, resp. $ε= 2$. Fourth, an experimental and theoretical analysis gives concrete results on the resistance of our approach to the plausible deniability and resistance against averaging attacks.

Nazatul Haque Sultan, Maryline Laurent, Vijay Varadharajan

For better data availability and accessibility while ensuring data secrecy, organizations often tend to outsource their encrypted data to the cloud storage servers, thus bringing the challenge of keyword search over encrypted data. In this paper, we propose a novel authorized keyword search scheme using Role-Based Encryption (RBE) technique in a cloud environment. The contributions of this paper are multi-fold. First, it presents a keyword search scheme which enables only the authorized users, having proper assigned roles, to delegate keyword-based data search capabilities over encrypted data to the cloud providers without disclosing any sensitive information. Second, it supports a multi-organization cloud environment, where the users can be associated with more than one organization. Third, the proposed scheme provides efficient decryption, conjunctive keyword search and revocation mechanisms. Fourth, the proposed scheme outsources expensive cryptographic operations in decryption to the cloud in a secure manner. Fifth, we have provided a formal security analysis to prove that the proposed scheme is semantically secure against Chosen Plaintext and Chosen Keyword Attacks. Finally, our performance analysis shows that the proposed scheme is suitable for practical applications.

Collins Mtita, Maryline Laurent, Damien Sauveron et al.

It is widely acknowledged that the proliferation of Unmanned Aerial Vehicles (UAVs) may lead to serious concerns regarding avionics safety, particularly when end-users are not adhering to air safety regulations. There are, however, domains in which UAVs may help to increase the safety of airplanes and the management of flights and airport resources that often require substantial human resources. For instance, Paris Charles de Gaulle airport (CDG) has more than 7,000 staff and supports 30,000 direct jobs for more than 60 million passengers per year (as of 2016). Indeed, these new systems can be used beneficially for several purposes, even in sensitive areas like airports. Among the considered applications are those that suggest using UAVs to enhance safety of on-ground airplanes; for instance, by collecting (once the aircraft has landed) data recorded by different systems during the flight (like the sensors of the Aircraft Data Networks - ADN) or by examining the state of airplane structure. In this paper, our proposal is to use UAVs, under the control of the airport authorities, to inventory and track various tagged assets, such as luggage, supplies required for the flights, and maintenance tools. The aim of our proposal is to make airport management systems more efficient for operations requiring inventory and tracking, along with increasing safety (sensitive assets such as refueling tanks, or sensitive pieces of luggage can be tracked), thus raising financial profit.