Identification et mise en oeuvre d'un consolidant de surface et d'un adhésif appliqués sur deux poissons momifiés égyptiens

Le Muséum National d'Histoire Naturelle de Paris possède une collection de poissons momifiés égyptiens (Perches du Nil) ramenés lors de l'expédition en Egypte de Napoléon Bonaparte, par Etienne Geoffroy Saint-Hilaire. Les spécimens de cette collection sont relativement biens conservés mais deux d'entre eux sont dans un état de conservation critique. Leur conservation à long terme nécessite l'application d'un consolidant de surface afin de limiter les altérations et d'un adhésif pour remettre en connexion les parties désolidarisées et redonner une lisibilité à l'"objet". Pour cela, il est important de connaître la composition physico-chimique de la peau et des écailles ainsi que les procédés de momification ayant été employés et la nature des substances utilisées. En effet, leur détermination est primordiale car l'adhésif et le consolidant ne devront pas interférer avec la composition physico-chimique du substrat, d'où l'intérêt de mener en amont une étude à la fois théorique et pratique. Une fois celle-ci achevée, différents adhésifs et consolidants seront préparés et testés en fonction de paramètres préalablement définis (Neutralité, élasticité, matité, pénétration limitée, etc.). Après avoir sélectionné l'adhésif et le consolidant le plus approprié et déterminer les méthodes de mise en oeuvre, il est procédé à leur application sur les momies de poissons. L'intervention de conservation-restauration sur des objets aussi fragiles est très délicate mais nécessaire à leur "survie".The National Natural History Museum (MNHN) of Paris possesses a collection of Egyptian mummified fishes (Nil perchs) returned during the Napoleon Bonaparte's expedition in Egypt, by Etienne Geoffroy Saint-Hilaire. The specimens of this collection are relatively well preserved but two of them are in a state of critical preservation. Their long-term preservation requires the application of a surface' strengthening to limit the alterations and of an adhesive to hand in connection the separated parts and restore the "object" 's legibility. For that purpose, it is important to know the skin and scales physico chemical composition as well as the mummification processes having been used (employed) and the nature of used substances. Indeed, their determination is essential because the adhesive and strengthening will not have to interfere with the substratum physico-chemical composition, where from the interest to lead upstream an at once theoretical and practical study. Once this one finished, various adhesives and consolidants will be prepared and tested according to parameters beforehand defined (Neutrality, elasticity, matt effect, penetration limited, etc.). Having selected the adhesive and strengthening it the most appropriate and having determined the methods of implementation, it is proceeded to their application on the fishes' mummies. The intervention of preservation-restoration on so fragile objects is very delicate but necessary for their "survival"

A Flexible Privacy-preserving Framework for Singular Value Decomposition under Internet of Things Environment

The singular value decomposition (SVD) is a widely used matrix factorization tool which underlies plenty of useful applications, e.g. recommendation system, abnormal detection and data compression. Under the environment of emerging Internet of Things (IoT), there would be an increasing demand for data analysis to better human's lives and create new economic growth points. Moreover, due to the large scope of IoT, most of the data analysis work should be done in the network edge, i.e. handled by fog computing. However, the devices which provide fog computing may not be trustable while the data privacy is often the significant concern of the IoT application users. Thus, when performing SVD for data analysis purpose, the privacy of user data should be preserved. Based on the above reasons, in this paper, we propose a privacy-preserving fog computing framework for SVD computation. The security and performance analysis shows the practicability of the proposed framework. Furthermore, since different applications may utilize the result of SVD operation in different ways, three applications with different objectives are introduced to show how the framework could flexibly achieve the purposes of different applications, which indicates the flexibility of the design.Comment: 24 pages, 4 figure

La place des contes dans les programmes scolaires

Les contes ont longtemps été transmis oralement. Au milieu du xvie siècle, Straparola livre les premières transcriptions littéraires de contes populaires vénitiens. En 1625, le napolitain Basile rédige Lo Cunto de li cunti (Le Conte des contes regroupant cinquante contes de fées), plus communément désigné Pentamerone. Le genre du conte de fées est né en France au xviIe siècle, et en marge du conte merveilleux de Charles Perrault et du « Cercle des conteuses » (Mademoiselle Lhéritier, nièce de..

Un exemple de conservation-restauration de momies animales

Cette étude de cas de deux poissons momifiés égyptiens, dont l’état critique de conservation nécessitait une intervention de conservation-restauration afin de leur redonner une certaine lisibilité, a été l’occasion de s’interroger sur les choix à opérer, sur les limites de l’intervention et sur la légitimité à privilégier la pérennité sur la réversibilité.This case study of two Egyptian mummified fish, whose critical state of conservation required a conservation/restoration intervention so as to give them a certain ‘definition’ once again, has been the opportunity to think about firstly the choices that needed to be made, secondly about the limits of this type of intervention, and finally about the question whether it’s better to ensure long-term conservation rather than reversibility

Secure biometric authentication with improved accuracy

We propose a new hybrid protocol for cryptographically secure biometric authentication. The main advantages of the proposed protocol over previous solutions can be summarised as follows: (1) potential for much better accuracy using different types of biometric signals, including behavioural ones; and (2) improved user privacy, since user identities are not transmitted at any point in the protocol execution. The new protocol takes advantage of state-of-the-art identification classifiers, which provide not only better accuracy, but also the possibility to perform authentication without knowing who the user claims to be. Cryptographic security is based on the Paillier public key encryption scheme

Private inter-network routing for wireless sensor networks and the Internet of Things

As computing becomes increasingly pervasive, different heterogeneous networks are connected and integrated. This is especially true in the Internet of Things (IoT) and Wireless Sensor Networks (WSN) settings. However, as different networks managed by different parties and with different security requirements are integrated, security becomes a primary concern. WSN nodes, in particular, are often deployed "in the open", where a potential attacker can gain physical access to the device. As nodes can be deployed in hostile or difficult scenarios, such as military battlefields or disaster recovery settings, it is crucial to avoid escalation from successful attacks on a single node to the whole network, and from there to other connected networks. It is therefore crucial to secure the communication within the WSN, and in particular, maintain context information, such as the network topology and the location and identity of base stations (which collect data gathered by the sensors) private. In this paper, we propose a protocol achieving anonymous routing between different interconnected IoT or WSN networks, based on the Spatial Bloom Filter (SBF) data structure. The protocol enables communications between the nodes through the use of anonymous identifiers, thus hiding the location and identity of the nodes within the network. The proposed routing strategy preserves context privacy, and prevents adversaries from learning the network structure and topology, as routing information is encrypted using a homomorphic encryption scheme, and computed only in the encrypted domain. Preserving context privacy is crucial in preventing adversaries from gaining valuable network information from a successful attacks on a single node of the network, and reduces the potential for attack escalation

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data