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random subset assignment scheme clearly has advantages over the basic probabilistic and the q-composite schemes The random subset assignment scheme also has several advantages over the random pairwise keys scheme [4] In terms of security performance, the random pairwise keys scheme does not allow reuse of the same key by multiple pairs of sensor nodes [4] Thus, the compromise of some sensor nodes does not lead to the compromise of direct keys shared between noncompromised nodes However, the random pairwise keys scheme has a strict upper bound on the network size for a given storage constraint and a desired probability to share common keys between nodes In contrast, the random subset assignment scheme allows the network to grow by trading off with security Moreover, in the random subset assignment scheme, sensor nodes can be added dynamically without having to contact the previously deployed sensor nodes.

However, in the random pairwise keys scheme, if it is necessary to dynamically deploy sensor nodes, the setup server has to either reserve space for sensor nodes that may never be deployed, which reduces the probability that two deployed nodes share a common key, or inform some previously deployed nodes of additional pairwise keys, which introduces additional communication overhead Thus, in certain applications, the random subset assignment scheme is a more attractive choice than the random pairwise keys scheme 5334 Grid-Based Key Predistribution Grid-based key predistribution is another instantiation of the general framework Suppose a sensor network has at most N sensor nodes The grid-based scheme constructs an m m grid generates p and 2m bivariate polynomials {fic (x, y), fir (x, y)}i 0, .., m 1 , where m d N e As shown in Figure 5.

5(a), each row i in the grid is associated with a polynomial fir (x, y), and each column i is associated with a polynomial fic (x, y) The setup server assigns each.

DAVE GLASER, VICE PRESIDENT, GLOBAL SERVICES, CYBERSOURCE As a fraud manager, you ve got a tough job. You re pressured to deliver on multiple often con icting objectives, simultaneously. And, the business dynamics change constantly with new products, business models, fraudster tactics, and staff changes. So, what should you measure Where should you focus your energies How do you know if you are doing good We see two common traps. The rst is the tendency to focus on one metric, such as chargeback rate, as the singular measure of your success. The second trap is the tendency to adopt someone else s benchmark rate as your own goal. In reality, fraud management is a complex system and is highly sensitive to the very speci c context of your business. So you want to be thinking more broadly about (1) your process, and (2) your pro le, to really understand what good means. Thinking about your process means understanding the balance between several key metrics your fraud rate, sales dollars lost to suspected fraud (reject rate), and overhead costs due to manual review and how those work together to make you more profitable. In the simplest view, you can aim to be more or less tight or loose in controlling fraud. Tight is not always good. Tight tends to increase reject rates and review rates. You can be too tight if you are rejecting many good orders in a singular quest to drive down fraud. To nd your optimal balance, you need to

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I organized this list of questions by assigning each question to one of eight categories: General Excel The Visual Basic Editor Sub procedures and Function procedures Objects, properties, methods, and events VBA instructions UserForms Add-ins CommandBars In some cases, my classifications are rather arbitrary; a question could justifiably be assigned to other categories. Moreover, questions within each category are listed in no particular order. By the way, most of the information in this chapter is discussed in greater detail in other chapters in this book.