EverCrypt: A Fast, Verified, Cross-Platform Cryptographic Provider

We present EverCrypt: a comprehensive collection of verified, high-performance cryptographic functionalities available via a carefully designed API. The API provably supports agility (choosing between multiple algorithms for the same functionality) and multiplexing (choosing between multiple implementations of the same algorithm). Through abstraction and zero-cost generic programming, we show how agility can simplify verification without sacrificing performance, and we demonstrate how C and assembly can be composed and verified against shared specifications. We substantiate the effectiveness of these techniques with new verified implementations (including hashes, Curve25519, and AES-GCM) whose performance matches or exceeds the best unverified implementations. We validate the API design with two high-performance verified case studies built atop EverCrypt, resulting in line-rate performance for a secure network protocol and a Merkle-tree library, used in a production blockchain, that supports 2.7 million insertions/sec. Altogether, EverCrypt consists of over 124K verified lines of specs, code, and proofs, and it produces over 29K lines of C and 14K lines of assembly code

Certification formelle de preuves cryptographiques basées sur les séquences de jeux

Les séquences de jeux sont une méthodologie établie pour structurer les preuves cryptographiques. De telles preuves peuvent être formalisées rigoureusement en regardant les jeux comme des programmes probabilistes et en utilisant des méthodes de vérification de programmes. Cette thèse décrit CertiCrypt, un outil permettant la construction et vérification automatique de preuves basées sur les jeux. CertiCrypt est implémenté dans l'assistant à la preuve Coq, et repose sur de nombreux domaines, en particulier les probabilités, la complexité, l'algèbre, et la sémantique des langages de programmation. CertiCrypt fournit des outils certifiés pour raisonner sur l'équivalence de programmes probabilistes, en particulier une logique de Hoare relationnelle, une théorie équationnelle pour l'équivalence observationnelle, une bibliothèque de transformations de programme, et des techniques propres aux preuves cryptographiques, permettant de raisonner sur les évènements. Nous validons l'outil en formalisant les preuves de sécurité de plusieurs exemples emblématiques, notamment le schéma de chiffrement OAEP et le schéma de signature FDH.PARIS-MINES ParisTech (751062310) / SudocSudocFranceF

On the Efficacy of Differentially Private Few-shot Image Classification

There has been significant recent progress in training differentially private (DP) models which achieve accuracy that approaches the best non-private models. These DP models are typically pretrained on large public datasets and then fine-tuned on private downstream datasets that are relatively large and similar in distribution to the pretraining data. However, in many applications including personalization and federated learning, it is crucial to perform well (i) in the few-shot setting, as obtaining large amounts of labeled data may be problematic; and (ii) on datasets from a wide variety of domains for use in various specialist settings. To understand under which conditions few-shot DP can be effective, we perform an exhaustive set of experiments that reveals how the accuracy and vulnerability to attack of few-shot DP image classification models are affected as the number of shots per class, privacy level, model architecture, downstream dataset, and subset of learnable parameters in the model vary. We show that to achieve DP accuracy on par with non-private models, the shots per class must be increased as the privacy level increases. We also show that learning parameter-efficient FiLM adapters under DP is competitive with learning just the final classifier layer or learning all of the network parameters. Finally, we evaluate DP federated learning systems and establish state-of-the-art performance on the challenging FLAIR benchmark.Peer reviewe