Fully secure hidden vector encryption under standard assumptions

We propose a fully functional identity-based encryption scheme (IBE). The scheme has chosen ciphertext security in the random oracle model assuming an elliptic curve variant of the computational Diffie-Hellman problem. Our system is based on bilinear maps between groups. The Weil pairing

of statistical queries and correlating the responses. Statlstmal data banks are much less secure than most people beheve. Data encryption attempts to prevent unauthorized disclosure of confidential information in transit or m storage. This paper describes the general nature of controls of each type, the kinds

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We consider routing security in wireless sensor networks. Many sensor network routing protocols have been proposed, but none of them have been designed with security as agq1( We propose securitygcur forrouting in sensor networks, show how attacks agacks ad-hoc and peer-to-peer networks can

Current standard security practices do not provide substantial assurance that the end-to-end behavior of a computing system satisfies important security policies such as confidentiality. An end-to-end confidentiality policy might assert that secret input data cannot be inferred by an attacker

and authenticated information over an insecure network. These protocols are secure against active attacks, and have the property that the password is protected against off-line "dictionary" attacks. There are a number of other useful applications as well, including secure public telephones.

The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a precisely defined training interval, operating while the network runs; and (2) the disadvantage that they require nonlocal communication in the network being trained and are computationally expensive. These algorithms are shown to allow networks having recurrent connections to learn complex tasks requiring the retention of information over time periods having either fixed or indefinite length. 1 Introduction A major problem in connectionist theory is to develop learning algorithms that can tap the full computational power of neural networks. Much progress has been made with feedforward networks, and attention has recently turned to developing algorithms for networks with recurrent connections, wh...

This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance

-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown