Next talks

APRIL 2024

” Combining differential privacy and homomorphic encryption for privacy-preserving collaborative learning”  by Arnaud Grivet Sébert (CEA LIST)

Date : 05 april 2024
Room : Pétri/Turing

We present several collaborative learning frameworks that ensure the training data privacy through the combination of differential privacy(DP) and homomorphic encryption (HE). The first of these frameworks is SPEED (Secure, Private, and Efficient Deep learning), which is based on PATE (Private Aggregation of Teacher Ensembles) framework (from Semi-supervised knowledge transfer for deep learning from private training data, Papernot et al., 2016) and which advances state-of-the-art of private deep learning against a wider range of threats. The training data privacy is protected against both the honest-but-curious aggregation server, end-users and potentially colluding data owners.To further reduce the computation time of the homomorphic aggregation in SPEED, we introduce SHIELD (Secure and Homomorphic Imperfect Election via Lightweight Design), a probabilistic approximate algorithm for the argmax operator which is faster when homomorphically executed and whose inaccuracy is used as a feature to provably enable DP guarantees. To the best of our knowledge, it is the first work in which the design of an approximate algorithm is constructively usable as a degree of freedom to achieve better HE performance. Finally, we present a private federated learning protocol that makes use of both HE and DP. The quantisation of the noise due to encryption highly complicates the DP analysis. We solve this issue by designing a novel quantisation operator based on Poisson law and which commutes with the aggregation, thus acting like a post-processing that does not impact the DP guarantee.


” TreeSync: Authenticated Group Management for Messaging Layer Security”  by Théophile Wallez (Inria)

Date : 12 january 2024
Room : Amphitheatre Inria Rennes

Messaging Layer Security (MLS), currently undergoing standardization at the IETF, is an asynchronous group messaging protocol that aims to be efficient for large dynamic groups, while providing strong guarantees like forward secrecy (FS) and post-compromise security (PCS). While prior work on MLS has extensively studied its group key establishment component (called TreeKEM), many flaws in early designs of MLS have stemmed from its group integrity and authentication mechanisms that are not as well-understood. In this work, we identify and formalize TreeSync: a sub-protocol of MLS that specifies the shared group state, defines group management operations, and ensures consistency, integrity, and authentication for the group state across all members. We present a precise, executable, machine-checked formal specification of TreeSync, and show how it can be composed with other components to implement the full MLS protocol. Our specification is written in F* and serves as a reference implementation of MLS; it passes the RFC test vectors and is interoperable with other MLS implementations. Using the DY* symbolic protocol analysis framework, we formalize and prove the integrity and authentication guarantees of TreeSync, under minimal security assumptions on the rest of MLS. Our analysis identifies a new attack and we propose several changes that have been incorporated in the latest MLS draft. Ours is the first testable, machine-checked, formal specification for MLS, and should be of interest to both developers and researchers interested in this upcoming standard.


” The VEREFOO Network Security Automation Approach”  by Prof. Riccardo Sisto (Politecnico di Torino)

Date : 19 january 2024
Room : Aurigny

Network softwarization and virtualization are making networks more and more dynamic, opening the possibility of extremely fast reconfigurations and of enhanced automation in their management.
This enhanced dynamism is a great opportunity, for example for prompt reaction to security attacks or to changing demands from users, but at the same time it introduces new challenges, such as how to guarantee that security policies are always correctly implemented and that resources are used efficiently in such rapidly changing systems.
VEREFOO (VErified REfinement and Optimized Orchestration) is an approach for policy-based network security automation in virtualized networks, developed at the Turin Polytechnic by the NetGroup (Computer Networks Group). VEREFOO enables automatic refinement of security policies into network configurations, providing at the same time formally verified and optimized solutions.
The talk presents VEREFOO, by explaining how it works, by illustrating its state of the art, i.e., all the major results already achieved, and by discussing the VEREFOO ongoing research activities.

MARCH 2024

” LDP-Auditor: Empirical Local Privacy Loss Estimation”  by Heber Hwang-Arcolezi (Inria)

Date : 22 march 2024
Room : Pétri/Turing

While the existing literature on Differential Privacy (DP) auditing predominantly focuses on the centralized model (e.g., in auditing the DP-SGD algorithm), we advocate for extending this approach to audit Local DP (LDP). In this talk, we delve into the auditing perspective of LDP, seeking to reveal the true cost of local privacy and empirically estimate the privacy loss incurred in practical scenarios. In-depth case studies will be presented to explore specific aspects of LDP auditing, including longitudinal studies and multidimensional data. Finally, we present a notable achievement of our LDP-Auditor framework, which is the discovery of a bug in a state-of-the-art LDP Python package. Overall, we aim to shed light on the sources of randomness and information loss in LDP and explore insights that can be gained by examining LDP through an auditing lens. This work has been presented at the TPDP workshop [1] and is available on arxiv [2].


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