Cloud file sharing using PREaaS

This paper proposes a new method of features extraction for handwritten, printed and isolated numeral recognition. It is essential today for a company to store its data in an encrypted way when it uses Cloud Computing. However, the manipulation of this encrypted data remains complex, and it is very difficult in this case to be able to share the encrypted data between different users. One of the solutions for sharing encrypted data is to use PRE (Proxy Reencryption) which allows both the re-encryption of the data, but also the delegation of this operation by a third party via the use of a specific key. In this article, we propose a solution for sharing encrypted files between users that uses a classic storage system in the Cloud and PRE (re-encryption PRoxy). We present an improvement of an existing PRE algorithm by applying it to elliptical curves in order to improve its performance. Finally, we implement this architecture in the form of a cloud service called PREaaS (PRE as a Service) which allows this mechanism to be used on demand with an API

Time-Memory Trade-offs for Parallel Collision Search Algorithms

Parallel versions of collision search algorithms require a significant amount of memory to store a proportion of the points computed by the pseudo-random walks. Implementations available in the literature use a hash table to store these points and allow fast memory access. We provide theoretical evidence that memory is an important factor in determining the runtime of this method. We propose to replace the traditional hash table by a simple structure, inspired by radix trees, which saves space and provides fast look-up and insertion. In the case of many-collision search algorithms, our variant has a constant-factor improved runtime. We give benchmarks that show the linear parallel performance of the attack on elliptic curves discrete logarithms and improved running times for meet-in-the-middle applications

Logical Reasoning to Detect Weaknesses About SHA-1 and MD4/5

In recent years, studies about the SATisfiability Problem (short for SAT) were more and more numerous because of its conceptual simplicity and ability to express a large set of various problems. Within a practical framework, works highlighting SAT impli- cations in real world problems had grown significantly. In this way, a new field called logical cryptanalysis appears in the 2000s and consists in an algebraic cryptanalysis in a binary context thanks to SAT solving. This paper deals with this concept applied to cryptographic hash functions. We first present the logical cryptanalysis principle, and provide details about our encoding approach. In a second part, we put the stress on the contribution of SAT to analyze the generated problem thanks to the discover of logical inferences and so simplifications in order to reduce the computational complexity of the SAT solving. This is mainly realized thanks to the use as a preprocessor of learning and pruning techniques from the community. Third, thanks to a probabilistic reasoning applied on the formulas, we present a weakness based on the use of round constants to detect probabilistic relations as implications or equivalences between certain vari- ables. Finally, we present a practical framework to exploit these weaknesses through the inversions of reduced-step versions of MD4, MD5, SHA-0 and SHA-1 and open some prospects

A SAT-based approach for index calculus on binary elliptic curves

Logical cryptanalysis, first introduced by Massacci in 2000, is a viable alternative to common algebraic cryptanalysis techniques over boolean fields. With XOR operations being at the core of many cryptographic problems, recent research in this area has focused on handling XOR clauses efficiently. In this paper, we investigate solving the point decomposition step of the index calculus method for prime degree extension fields $\mathbb{F}_{2^n}$, using SAT solving methods. We experimented with different SAT solvers and decided on using WDSat, a solver dedicated to this specific problem. We extend this solver by adding a novel breaking symmetry technique and optimizing the time complexity of the point decomposition step by a factor of $m!$ for the $(m+1)$\textsuperscript{th} Semaev\u27s summation polynomial. While asymptotically solving the point decomposition problem with this method has exponential worst time complexity in the dimension $l$ of the vector space defining the factor base, experimental running times show that the the presented SAT solving technique is significantly faster than current algebraic methods based on Gröbner basis computation. For the values $l$ and $n$ considered in the experiments, the WDSat solver coupled with our breaking symmetry technique is up to 300 times faster then MAGMA\u27s F4 implementation, and this factor grows with $l$ and $n$

Utilisation de la Propagation de Contraintes Booléennes pour la Production de Sous-Clauses

http://www710.univ-lyon1.fr/~csolnonLa propagation de contraintes booléennes (BCP) est la technique la plus utile et la plus utilisÈe dans les solveurs SAT. Dans cet article, nous proposons une autre utilisation de cette technique dans le but de réduire, en termes de nombre de clauses et de longueur des clauses, la formule initiale. En considérant le graphe d'implications généré par la procédure BCP comme un arbre de résolution, nous pouvons déduire des sous-clauses de la formule initiale. Nous montrons ensuite, comment une telle extension peut être implémentée dans les solveurs actuels où la procédure BCP est utilisée à chaque noeud de l'arbre de recherche. Nous présentons une première implémentation de cette approche dans le cadre d'un pré-traitement pour le solveur Zchaff. Pour finir, des résultats comparatifs préliminaires montrant les points forts et les faiblesses de l'approche sont fournis sur certaines classes d'instances de nature structurées

Predictive models in emergency medicine and their missing data strategies: a systematic review

In the field of emergency medicine (EM), the use of decision support tools based on artificial intelligence has increased markedly in recent years. In some cases, data are omitted deliberately and thus constitute “data not purposely collected” (DNPC). This accepted information bias can be managed in various ways: dropping patients with missing data, imputing with the mean, or using automatic techniques (e.g., machine learning) to handle or impute the data. Here, we systematically reviewed the methods used to handle missing data in EM research. A systematic review was performed after searching PubMed with the query “(emergency medicine OR emergency service) AND (artificial intelligence OR machine learning)”. Seventy-two studies were included in the review. The trained models variously predicted diagnosis in 25 (35%) publications, mortality in 21 (29%) publications, and probability of admission in 21 (29%) publications. Eight publications (11%) predicted two outcomes. Only 15 (21%) publications described their missing data. DNPC constitute the “missing data” in EM machine learning studies. Although DNPC have been described more rigorously since 2020, the descriptions in the literature are not exhaustive, systematic or homogeneous. Imputation appears to be the best strategy but requires more time and computational resources. To increase the quality and the comparability of studies, we recommend inclusion of the TRIPOD checklist in each new publication, summarizing the machine learning process in an explicit methodological diagram, and always publishing the area under the receiver operating characteristics curve—even when it is not the primary outcome

Computer-aided screening of autism spectrum disorder: Eye-tracking study using data visualization and deep learning

Background: The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. Objective: This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening Methods: The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. Results: The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. Conclusions: Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders

Renormalisation as a function of clause lengths for solving random k-SAT

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Approches spécialisées pour la résolution de problèmes combinatoires fondamentaux

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