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Securing computing systems is a very broad topic. The issues we must consider come from several different areas: physical security, host and operating system security, network security, and application security. In order to address the issues in these areas, we must perform many tasks, such as ensuring good coding practices, enforcing good policies and procedures for auditing, and assuring, detecting, and reacting to attacks. Most of the technologies for securing distributed databases focus on the data itself and the communication paths. These protocols are in turn based on cryptographic building blocks and speci c implementation algorithms for these blocks. In this chapter, we explored the basic building blocks and associated algorithms. We then looked at some basic protocols for securing communications and data, examined several databasespeci c security vulnerabilities, examined some broader architectural issues, and looked at a typical deployment. As the amount of online data and the number of people granted access to it (worldwide) continue to increase at a staggering rate, security issues and technologies will only continue to grow in importance.

Click the OK button or press the Enter key. The Windows Registry Editor opens. In the left window pane, doubleclick the registry key labeled HKEY_LOCAL_MACHINE. If you can t nd it, do the following: a. In the left window pane of the Registry Editor, scroll to the top. b. If any of the HKEY registry keys are open as indicated by a minus sign (-) on their left side then close them by clicking that minus sign. When a registry key has been properly closed, it will have a plus sign (+) next to it. c. Repeat this process for the remaining HKEY registry keys until the only things visible in the left window pane are the ve HKEY keys (see Figure 1-1).

designed to deal with the practical issues involving (a) alternative models of change, (b) unequal intervals, (c) unequal numbers of persons in different groups, (d) nonrandom attrition, (e) the altering of measures over time, and (f) multiple outcomes This contemporary, model-based description of change can be used to clarify some problems inherent in observed rates of change The potential confounds in difference scores have been a key concern of previous methods using observed change scores or rate-of-change scores (eg, Bereiter, 1963; Burr & Nesselroade, 1990; Cronbach & Furby, 1970; Rogosa & Willett, 1985; Willett, 1990) This research has shown that when observed rates are used as outcomes in standard regression analyses, the results can be biased by several factors, including residual error, measurement error, regression to the mean, and egression from the mean (eg.

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Figure 8-19

, Allison, 1990; Nesselroade & Bartsch, 1977; Nesselroade & Cable, 1974; Nesselroade, Stiegler, & Baltes, 1980; Raykov, 1999; Williams & Zimmerman, 1996) These problems can be severe when using standard linear regression with timedependent variables (eg, Boker & McArdle, 1995; Hamagami & McArdle, 2000) One of the key reasons we present the contemporary modeling approach is to move beyond these classical problems Modern statistical procedures have been developed to minimize some of these problems by tting the model of an implied trajectory over time directly to the observed scores Alternative mathematical forms of growth can be considered using different statistical restrictions From such formal assumptions we can write the set of expectations for the means, variances, and covariances for all observed scores, and use these expectations to identify, estimate, and examine the goodness-of- t of latent variable models representing change over time.

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Most of these models discussed here are based on tting observed raw-score longitudinal growth data to a theoretical model using likelihood-based techniques (as in Little & Rubin, 1987; McArdle & Bell, 2000) In general, we nd it convenient to describe the data using the observed change scores (de ned as Yn / t), but we make inferences about the underlying growth processes by directly estimating parameters of the latent change scores (de ned as yn / t) In a recent and important innovation, Meredith and Tisak (1990) showed how the Tuckerized curve models (so named in recognition of Tucker s seminal contributions) could be represented and tted using structural equation modeling of common factors These growth modeling results were important because this made it possible to represent a wide range of alternative growth models This work also led to interest in methodological and substantive studies of growth processes.

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To derive the full bene t of this text, the reader should have a basic knowledge of algebra, logarithms, and some essentials of trigonometry. To that end we have developed this appendix. The objective is to bring the reader along, and not to provide an exhaustive primer on basic mathematics. There are four subsections: