Characteristics for Software Optimization Projects

The increasing of the software systems complexity imposes the identification and implementation of some methods and techniques in order to manage it. The software optimization project is a way in which the software complexity is controlled. The software optimization project must face to the organization need to earn profit. The software optimization project is an integrated part of the application cycle because share same resources, depends on other stages and influences next phases. The optimization project has some particularities because it works on an finished product around its quality. The process is quality and performance oriented and it assumes that the product life cycle is almost finished.optimization, software, project management, quality, performance

Characteristics for Software Optimization Projects

The increasing of the software systems complexity imposes the identification and implementation of some methods and techniques in order to manage it. The software optimization project is a way in which the software complexity is controlled. The software optimization project must face to the organization need to earn profit. The software optimization project is an integrated part of the application cycle because share same resources, depends on other stages and influences next phases. The optimization project has some particularities because it works on an finished product around its quality. The process is quality and performance oriented and it assumes that the product life cycle is almost finished

Préserver la vie privée des individus grâce aux Systèmes Personnels de Gestion des Données

Riding the wave of smart disclosure initiatives and new privacy-protection regulations, the Personal Cloud paradigm is emerging through a myriad of solutions offered to users to let them gather and manage their whole digital life. On the bright side, this opens the way to novel value-added services when crossing multiple sources of data of a given person or crossing the data of multiple people. Yet this paradigm shift towards user empowerment raises fundamental questions with regards to the appropriateness of the functionalities and the data management and protection techniques which are offered by existing solutions to laymen users. Our work addresses these questions on three levels. First, we review, compare and analyze personal cloud alternatives in terms of the functionalities they provide and the threat models they target. From this analysis, we derive a general set of functionality and security requirements that any Personal Data Management System (PDMS) should consider. We then identify the challenges of implementing such a PDMS and propose a preliminary design for an extensive and secure PDMS reference architecture satisfying the considered requirements. Second, we focus on personal computations for a specific hardware PDMS instance (i.e., secure token with mass storage of NAND Flash). In this context, we propose a scalable embedded full-text search engine to index large document collections and manage tag-based access control policies. Third, we address the problem of collective computations in a fully-distributed architecture of PDMSs. We discuss the system and security requirements and propose protocols to enable distributed query processing with strong security guarantees against an attacker mastering many colluding corrupted nodes.Surfant sur la vague des initiatives de divulgation restreinte de données et des nouvelles réglementations en matière de protection de la vie privée, le paradigme du Cloud Personnel émerge à travers une myriade de solutions proposées aux utilisateurs leur permettant de rassembler et de gérer l'ensemble de leur vie numérique. Du côté positif, cela ouvre la voie à de nouveaux services à valeur ajoutée lors du croisement de plusieurs sources de données d'un individu ou du croisement des données de plusieurs personnes. Cependant, ce changement de paradigme vers la responsabilisation de l'utilisateur soulève des questions fondamentales quant à l'adéquation des fonctionnalités et des techniques de gestion et de protection des données proposées par les solutions existantes aux utilisateurs lambda. Notre travail aborde ces questions à trois niveaux. Tout d'abord, nous passons en revue, comparons et analysons les alternatives de cloud personnel au niveau des fonctionnalités fournies et des modèles de menaces ciblés. De cette analyse, nous déduisons un ensemble général d'exigences en matière de fonctionnalité et de sécurité que tout système personnel de gestion des données (PDMS) devrait prendre en compte. Nous identifions ensuite les défis liés à la mise en œuvre d'un tel PDMS et proposons une conception préliminaire pour une architecture PDMS étendue et sécurisée de référence répondant aux exigences considérées. Ensuite, nous nous concentrons sur les calculs personnels pour une instance matérielle spécifique du PDMS (à savoir, un dispositif personnel sécurisé avec un stockage de masse de type NAND Flash). Dans ce contexte, nous proposons un moteur de recherche plein texte embarqué et évolutif pour indexer de grandes collections de documents et gérer des politiques de contrôle d'accès basées sur des étiquettes. Troisièmement, nous abordons le problème des calculs collectifs dans une architecture entièrement distribuée de PDMS. Nous discutons des exigences d'architectures système et de sécurité et proposons des protocoles pour permettre le traitement distribué des requêtes avec de fortes garanties de sécurité contre un attaquant maîtrisant de nombreux nœuds corrompus

Data Leakage Mitigation of User-Defined Functions on Secure Personal Data Management Systems

National audiencePersonal Data Management Systems (PDMSs) arrive at a rapid pace providing individuals with appropriate tools to collect, manage and share their personal data. At the same time, the emergence of Trusted Execution Environments (TEEs) opens new perspectives in solving the critical and conflicting challenge of securing users' data while enabling a rich ecosystem of data-driven applications. In this paper, we propose a PDMS architecture leveraging TEEs as a basis for security. Unlike existing solutions, our architecture allows for data processing extensiveness through the integration of any userdefined functions, albeit untrusted by the data owner. In this context, we focus on aggregate computations of large sets of database objects and provide a first study to mitigate the very large potential data leakage. We introduce the necessary security building blocks and show that an upper bound on data leakage can be guaranteed to the PDMS user. We then propose practical evaluation strategies ensuring that the potential data leakage remains minimal with a reasonable performance overhead. Finally, we validate our proposal with an Intel SGX-based PDMS implementation on real data sets

Can non-small cell lung cancer histologic subtypes predict survival? A single institution experience

Introduction. The latest histological classification of lung adenocarcinoma includes lepidic, acinar, papillary, micropapillary, and solid as subtypes. Testing these subtypes for their prognostic and predictive value is an ongoing scientific challenge. The present research article aims to describe the influence this classification has on patient survival. Materials and Methods. Thirty-three patients were included in the trial. The most important enrollment criterion was the clear specification of the adenocarcinoma subtype in the pathology report. Patients were stratified into three groups which included the adenocarcinoma pathological subtypes as follows: lepidic (LEP), acinar and papillary (ACN/PAP), and micropapillary/solid (MIP/SOL). The primary endpoint was progression-free survival. Other endpoints included overall survival. Results. The lepidic subtype of ADC had superior PFS and OS, regardless of stage. Papillary and acinar subtype showed an intermediate prognosis, whereas micropapillary and solid subtypes were the most aggressive. Conclusions. The experience of this single center confirmed data in the literature. Further studies are needed to demonstrate all the possible implications of this pathology classification

DISPERS: Securing Highly Distributed Queries on Personal Data Management Systems

National audiencePersonal Data Management Systems (PDMS) advance at a rapid pace allowing us to integrate all our personal data in a single place and use it for our benefit and for the benefit of the community. This leads to a significant paradigm shift since personal data become massively distributed and opens an important question: how to query this massively distributed data in an efficient, pertinent and privacy-preserving way? This demonstration proposes a fully-distributed PDMS called DISPERS, built on top of SEP2P [11], allowing users to securely and efficiently share and query their personal data. The demonstration platform graphically illustrates the query execution in details, showing that DISPERS leads to maximal system security with low and scalable overhead. Attendees are welcome to challenge the security provided by DISPERS using the proposed hacking tools

SEP2P: Secure and Efficient P2P Personal Data Processing

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