A general approach to securely querying XML

XML access control requires the enforcement of highly expressive access control policies to support schema-, document and object-specific protection requirements. Access control models for XML data can be classified in two major categories: node filtering and query rewriting systems. The first category includes approaches that use access policies to compute secure user views on XML data sets. User queries are then evaluated on those views. In the second category of approaches, authorization rules are used to transform user queries to be evaluated against the original XML data set. The pros and cons for these approaches have been widely discussed in the framework of XML access control standardization activities. The aim of this paper is to describe a model combining the advantages of these approaches and overcoming their limitations, suitable as the basis of a standard technique for XML access control enforcement. The model specification is given using a Finite State Automata, ensuring generality w.r.t. specific implementation techniques

Online Meta-learning by Parallel Algorithm Competition

The efficiency of reinforcement learning algorithms depends critically on a few meta-parameters that modulates the learning updates and the trade-off between exploration and exploitation. The adaptation of the meta-parameters is an open question in reinforcement learning, which arguably has become more of an issue recently with the success of deep reinforcement learning in high-dimensional state spaces. The long learning times in domains such as Atari 2600 video games makes it not feasible to perform comprehensive searches of appropriate meta-parameter values. We propose the Online Meta-learning by Parallel Algorithm Competition (OMPAC) method. In the OMPAC method, several instances of a reinforcement learning algorithm are run in parallel with small differences in the initial values of the meta-parameters. After a fixed number of episodes, the instances are selected based on their performance in the task at hand. Before continuing the learning, Gaussian noise is added to the meta-parameters with a predefined probability. We validate the OMPAC method by improving the state-of-the-art results in stochastic SZ-Tetris and in standard Tetris with a smaller, 10$\times$10, board, by 31% and 84%, respectively, and by improving the results for deep Sarsa($\lambda$) agents in three Atari 2600 games by 62% or more. The experiments also show the ability of the OMPAC method to adapt the meta-parameters according to the learning progress in different tasks.Comment: 15 pages, 10 figures. arXiv admin note: text overlap with arXiv:1702.0311

Securely updating XML

We study the problem of updating XML repository through security views. Users are provided with the view of the repository schema they are entitled to see. They write update requests over their view using the XUpdate language. Each request is processed in two rewriting steps. First, the XPath expression selecting the nodes to update from the view is rewritten to another expression that only selects nodes the user is permitted to see. Second the XUpdate query is refined according to the write privileges held by the user

A general approach to securely querying XML

Access control models for XML data can be classified in two major categories: node filtering and query rewriting systems. The first category includes approaches that use access policies to compute secure user views on XML data sets. User queries are then evaluated on those views. In the second category of approaches, authorization rules are used to transform user queries to be evaluated against the original XML dataset. The aim of this paper is to describe a model combining the advantages of these approaches and overcoming their limitations. The model specification is given using a Finite State Automata, ensuring generality and easiness of standardization w.r.t. specific implementation techniques

The Benefits of Central Bank's Political Independence.

This paper analyzes the relationship between a central banker and his partisan political principals. Incentive contracts for central bankers are not designed by social planners but by partisan political principals who obey to their own incentives.CENTRAL BANKS ; MONETARY POLICY ; POLITICS

Interoperability, Safety and Security in IoT: Third International Conference, InterIoT 2017, and Fourth International Conference, SaSeIot 2017, Valencia, Spain, November 6-7, 2017, Proceedings

International audienceThis book constitutes the refereed post-conference proceedings of the Third International Conference on Interoperability, InterIoT 2017, which was collocated with SaSeIoT 2017, and took place in Valencia, Spain, in November 2017. The 14 revised full papers were carefully reviewed and selected from 22 submissions and cover all aspects of the latest research findings in the area of Internet of Things (IoT