Tracking Quantiles of Network Data Streams with Dynamic Operations

Identity-based remote data auditing schemes can verify data integrity and provide a simple identity authentication and management for multiple users. However, prior works on identity-based remote data auditing lack the support of dynamic operations. In these schemes, tag generation is linked to the index of data block, which is related to update operations such as modification, insertion and deletion. If users perform dynamic operations on a data block, the tags of all subsequent blocks need to be modified. It means that if users want to update data on a big data platform, they have to download the whole file, update the file and send the updated file to the big data platform. Such pattern will bring huge communication overhead. In this paper, we propose an identity-based dynamic data auditing scheme which supports dynamic data operations, including modification, insertion and deletion. As far as we know, there is still no other identity-based data auditing scheme that supports dynamic operations. In particular, to achieve efficient dynamic operations, we use the data structure of Merkle hash tree for block tag authentication, which helps update data with integrity assurance. Analyses of security and performance show that the proposed scheme is efficient and secure.

Various aspects of the neutron spectroscopy problem are studied in this research. Theoretically, the neutron emission problem transforms into a limited angle Radon transform problem. In order to solve this ill posed problem, various algorithms are tested spanning two different techniques; algebraic reconstruction as well as Monte-Carlo methods. The developed algorithms are Stochastic Gradient Approximation (SGA) method, Simultaneous Perturbation Stochastic Approximation (SPSA) method, and Time of Flight (TOF) method.

Cloud computing has been envisioned as the next-generation architecture of IT enterprise. However the security problem of Cloud computing hinder itself from large-scale application. One major challenge is to verify the integrity of data at untrusted server under the condition of supporting public auditability and dynamic data operation. This paper analyzed the problems of existing schemes, introduced commutative hash into Merkle hash tree, aggregated basic block into subblock and block, constructed a kind of hierarchical Merkle hash tree by using the concept of hierarchical structure in granular computing and proposed an improved scheme to offer effective, multi-granular dynamic operation. We analyzed the efficiency and security of the protocol. The analysis result showed that the protocol is efficient and security.

A self-equilibrium Friedman-like urn via stochastic approximation

This paper is a continuation of our papers [9], [10] and [11] and is concerned with a Robbins-Monro type stochastic approximation method when a sequence of dependently distributed random vectors is given. The method of stochastic approximation has been first proposed by H. Robbins and S. Monro ([5]) and its modifications have been thereafter given by many authors. A typical one of them is as follows. Suppose that an RN-valued random vector Y„(x) can be observed at xERN and each instant n, and the expected value of Y n(X), denoted by E[Y„(x)]=1/17,(x), is unknown to us. Assuming that the equation itin(x)=0 has a solution x=8,, for each n=1, 2, • , it is desire to estimate 0,, for sufficiently large n on the basis of observed values Y1(X0), Y2(Xi), Y.+I(X,i), ••• at the points X„ Xi, •-• , X„, ••• which are produced by the following recurrence relation,

23 Stochastic approximation

This chapter is concerned with decentralized and asynchronous forms of the stochastic approximation algorithms, a relatively new area of research. Compared with the rapid progress and extensive literature in stochastic approximation methods, the study of parallel stochastic approximations is still in its infancy. Perhaps the first work on the subject was [14], which dealt with a very particular class of algorithms, where similar computations were done by several processors and convex combinations were taken. The general ideas of weak convergence theory were applied to a fairly broad class of realistic algorithms in [111, 112]. The general ideas presented there and in [105, 171] form the basis of this chapter. Analogously to the problems in Chapter 8, those methods can handle correlated and state dependent noise, delays in communication, and asynchronous and distributed network forms. Various examples are given in Section 1. In the basic model, there are several processors; each one is responsible for the updating of only a part of the parameter vector. There might be overlaps in that several processors contribute to the updating of the same component of the parameter. Such models were treated in [105, 171, 172]. For a similar model, the problem of finding zeros of a nonlinear function with noisy observations via parallel processing methods and with random truncation bounds was treated in [201]. An attempt to get real-time implementable procedures via pipelining (see Section 1.2) of communication and computation for algorithms with “delayed” observations was in [203].

An application of an algorithm of stochastic approximation (SA) to statistical circuit design (yield optimisation) is proposed. Fast initial convergence of the algorithm, typical for all SA methods, has been practically demonstrated. The SA methods offer a theoretical background for the statistical circuit design, which has been lacking in mostly heuristic algorithms developed so far.

Near real-time estimation of end-to-end performance in converged fixed-mobile networks

The independent operation of mobile and fixed network segments is one of the main barriers that prevents improving network performance while reducing capital expenditures coming from overprovisioning. In particular, a coordinated dynamic network operation of both network segments is essential to guarantee end-to-end Key Performance Indicators (KPI), on which new network services rely on. To achieve such dynamic operation, accurate estimation of end-to-end KPIs is needed to trigger network reconfiguration before performance degrades. In this paper, we present a methodology to achieve an accurate, scalable, and predictive estimation of end-to-end KPIs with sub-second granularity near real-time in converged fixed-mobile networks. Specifically, we extend our CURSA-SQ methodology for mobile network traffic analysis, to enable converged fixed-mobile network operation. CURSA-SQ combines simulation and machine learning fueled with real network monitoring data. Numerical results validate the accuracy, robustness, and usability of the proposed CURSA-SQ methodology for converged fixed-mobile network scenarios.

Cloud storage system enables storing of data in the cloud server efficiently and makes the user to work with the data without any trouble of the resources. In the existing system, the data are stored in the cloud using dynamic data operation with computation which makes the user need to make a copy for further updating and verification of the data loss. An efficient distributed storage auditing mechanism is planned which over comes the limitations in handling the data loss. In this paper the partitioning method is proposed for the data storage which avoids the local copy at the user side by using partitioning method. This method ensures high cloud storage integrity, enhanced error localization and easy identification of misbehaving server. To achieve this, remote data integrity checking concept is used to enhance the performance of cloud storage. In nature the data are dynamic in cloud; hence this work aims to store the data in reduced space with less time and computational cost.

As data sharing has become one of the most popular services offered by cloud storage, designing public auditing mechanisms for integrity of shared data becomes more important. Two problems which arise in shared data auditing include preserving users identity and collusion resistant revocation of users. When data stored at the cloud is shared among a group of users, different users may modify and sign different data blocks which leaks signer identities to the public verifier. Also, when a user is revoked from the group, signatures generated by this user should be re-signed by the cloud server using re-signature keys. In addition, collusion of cloud server and the revoked user should leak no information about the private key of other users. In this paper, by employing a new proxy re-signature scheme, we propose a public shared data auditing mechanism that provides identity privacy and collusion resistant user revocation, simultaneously. The proposed protocol requires only lightweight computations at the user side for signing data blocks in real-time online phase. Moreover, our protocol supports large dynamic group of users, batch auditing and dynamic data operations. Experimental results demonstrate excellent efficiency of our scheme in comparison to the state of the art.

On the estimation of the adjustment coefficient in risk theory by means of stochastic approximation procedures

Necessary and sufficient conditions for a stochastic approximation method

In this paper, we propose an automatic selection of the bandwidth of the recursive kernel estimators of a regression function defined by the stochastic approximation algorithm. We showed that, using the selected bandwidth and the stepsize which minimize the mean weighted integrated squared error, the recursive estimator will be better than the non‐recursive one for small sample setting in terms of estimation error and computational costs. We corroborated these theoretical results through simulation study and a real dataset.