Hierarchical Role-Based Access Control with Homomorphic Encryption for Database as a Service

Database as a service provides services for accessing and managing customers data which provides ease of access, and the cost is less for these services. There is a possibility that the DBaaS service provider may not be trusted, and data may be stored on untrusted server. The access control mechanism can restrict users from unauthorized access, but in cloud environment access control policies are more flexible. However, an attacker can gather sensitive information for a malicious purpose by abusing the privileges as another user and so database security is compromised. The other problems associated with the DBaaS are to manage role hierarchy and secure session management for query transaction in the database. In this paper, a role-based access control for the multitenant database with role hierarchy is proposed. The query is granted with least access privileges, and a session key is used for session management. The proposed work protects data from privilege escalation and SQL injection. It uses the partial homomorphic encryption (Paillier Encryption) for the encrypting the sensitive data. If a query is to perform any operation on sensitive data, then extra permissions are required for accessing sensitive data. Data confidentiality and integrity are achieved using the role-based access control with partial homomorphic encryption.Comment: 11 Pages,4 figures, Proceedings of International Conference on ICT for Sustainable Developmen

Effect of matrix parameters on mesoporous matrix based quantum computation

We present a solid state implementation of quantum computation, which improves previously proposed optically driven schemes. Our proposal is based on vertical arrays of quantum dots embedded in a mesoporous material which can be fabricated with present technology. We study the feasibility of performing quantum computation with different mesoporous matrices. We analyse which matrix materials ensure that each individual stack of quantum dots can be considered isolated from the rest of the ensemble-a key requirement of our scheme. This requirement is satisfied for all matrix materials for feasible structure parameters and GaN/AlN based quantum dots. We also show that one dimensional ensembles substantially improve performances, even of CdSe/CdS based quantum dots

Mesoporous matrices for quantum computation with improved response through redundance

We present a solid state implementation of quantum computation, which improves previously proposed optically driven schemes. Our proposal is based on vertical arrays of quantum dots embedded in a mesoporous material which can be fabricated with present technology. The redundant encoding typical of the chosen hardware protects the computation against gate errors and the effects of measurement induced noise. The system parameters required for quantum computation applications are calculated for II-VI and III-V materials and found to be within the experimental range. The proposed hardware may help minimize errors due to polydispersity of dot sizes, which is at present one of the main problems in relation to quantum dot-based quantum computation. (c) 2007 American Institute of Physics

Coupled ice-ocean modeling and predictions

We review the coupled ice-ocean modeling activities aimed at predictions, both in the near term (days to a week) and in the long term (seasonal to decadal) of the polar oceans. First the state of the knowledge of potential predictability is exposed, then an overview is given of the tools available for carrying out such predictions: the observations that can be used to initialize actual predictions, the coupled ice-ocean–modeling, including the fully-coupled Earth System Models for long-term predictions, and data-assimilation techniques. Finally, the performance of existing prediction systems is reviewed, showing that, although more predictive capability remains than what is presently achieved, both the near- and long-term forecasts show skill over trivial predictors. Parallel efforts should therefore be invested into acquiring more observations of the ocean and sea ice, developing new models both in standalone and coupled mode, and improving the data-assimilation techniques

Transactions and updates in deductive databases

n this paper we develop a new approach providing a smooth integration of extensional updates and declarative query language for deductive databases. The approach is based on a declarative speci cation of updates in rule bodies. Updates are not executed as soon are evaluated. Instead, they are collectedand then applied to the database when the query evaluation is completed. We call this approach non-immediate update semantics. We provide a top down and equivalent bottom-up semantics which re ect the corresponding computation models. We also package set of updates into transactions and we provide a formal semantics for transactions. Then, in order to handle complex transactions, we extend the transaction language with control constructors still perserving formal semantics and semantics equivalence

Flexible Resolution of Authorisation Conflicts in Distributed Systems

Flexible Resolution of Authorisation Conflicts in Distributed System

Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to coping with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations of stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal of identifying or improving the model dynamics, building a surrogate or reduced model, or producing forecasts solely from observations of the physical model

Mesopore etching under supercritical conditions – A shortcut to hierarchically porous silica monoliths

Hierarchically porous silica monoliths are obtained in the two-step Nakanishi process, where formation of a macro microporous silica gel is followed by widening micropores to mesopores through surface etching. The latter step is carried out through hydrothermal treatment of the gel in alkaline solution and necessitates a lengthy solvent exchange of the aqueous pore fluid before the ripened gel can be dried and calcined into a mechanically stable macro mesoporous monolith. We show that using an ethanol water (95.6/4.4, v/v) azeotrope as supercritical fluid for mesopore etching eliminates the solvent exchange, ripening, and drying steps of the classic route and delivers silica monoliths that can withstand fast heating rates for calcination. The proposed shortcut decreases the overall preparation time from ca. one week to ca. one day. Porosity data show that the alkaline conditions for mesopore etching are crucial to obtain crack-free samples with a narrow mesopore size distribution. Physical reconstruction of selected samples by confocal laser scanning microscopy and subsequent morphological analysis confirms that monoliths prepared via the proposed shortcut possess the high homogeneity of silica skeleton and macropore space that is desirable in adsorbents for flow-through applications

FastLAS: scalable inductive logic programming incorporating domain-specific optimisation criteria

Inductive Logic Programming (ILP) systems aim to find a setof logical rules, called a hypothesis, that explain a set of ex-amples. In cases where many such hypotheses exist, ILP sys-tems often bias towards shorter solutions, leading to highlygeneral rules being learned. In some application domains likesecurity and access control policies, this bias may not be de-sirable, as when data is sparse more specific rules that guaran-tee tighter security should be preferred. This paper presents anew general notion of ascoring functionover hypotheses thatallows a user to express domain-specific optimisation criteria.This is incorporated into a new ILP system, calledFastLAS,that takes as input a learning task and a customised scoringfunction, and computes an optimal solution with respect tothe given scoring function. We evaluate the accuracy of Fast-LAS over real-world datasets for access control policies andshow that varying the scoring function allows a user to tar-get domain-specific performance metrics. We also compareFastLAS to state-of-the-art ILP systems, using the standardILP bias for shorter solutions, and demonstrate that FastLASis significantly faster and more scalable