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The flush() method can take an optional parameter that represents the disk space you anticipate needing. By using this parameter, you can limit user-facing dialog boxes to a single request. For example, say you store 150KB, ush, store another 150KB, ush, and store a nal 50KB and ush. With a default limit of 100KB, this results in three dialog boxes, rst to expand from 100KB to 150KB, then to expand from 150KB to 300KB, and nally to expand from 300KB to 350KB. Instead, you could even before storing data to the SharedObject ush while requesting 350KB of space. This presents a dialog box, but then (assuming the user accepts) the rest of the ush operations proceed without requiring user intervention, because the total space of 350KB was already allocated.



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Then each chain will, on average, be of length 271-s. In practice we would want to set a limit, on the maximum length of a chain and reject, any chain that exceeds the limit. Using distinguished points, the pre-computation is similar to the case

Using ush()

Use the minDiskSpace argument to request the total space you anticipate needing, in bytes. The return type lets you know if you need to wait for the user to accept or deny the request or if the operation failed. flush() can also throw an Error or dispatch an asynchronous NetStatusEvent error if the user speci ed to globally deny these requests or the dialog box can t be shown. The possible return types are de ned by static properties of the SharedObjectFlushStatus class:





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where l j is the length of chain j (that is, the number of elements computed before a distinguished point was found). We must also keep track of the maximum length of any chain within a table; for table i, denote this as Mi. Now suppose that r computers are available. Then each computer can search one of the T tables of chains. Computer i only needs to know the function Fi along with the ciphertext C and Mi, as well as the definition of a distinguished point. In particular, the triples in equation (4.5) do not need to be transmitted to any of the r computers, saving significant bandwidth and reducing the storage requirement on the individual computers. Each computer then proceeds with the attack as described above, with the exception that instead of looking for a matching EPj at each step, a distinguished point is sought. If computer i finds such a point within Mi iterations, the distinguished point is returned. Then secondary testing is necessary to determine whether the putative solution is an actual endpoint from table i or a false alarm. This secondary testing requires access t o all (SPj,E P j , l j ) triples in (4.5). Note that the overall work for secondary testing can be adjusted by selecting the definition of a distinguished point appropriately. If an endpoint is found, the process of attempting to recover K from the corresponding starting point proceeds exactly as in the non-distinguished point case discussed above.

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In this chapter, one can find a uniform and incremental algorithm for data organization. This method is based on formal concept analysis. Among the exponential number of concepts existing between elementary data, we only select optimal ones. We explained here how we can use these optimal concepts to extract association rules, or entities, from an instance of a database, or to find the best

SharedObjectFlushStatus.FLUSHED is returned if the shared object has been success-

fully written to disk. When calling flush(), you can check to see if user veri cation is necessary, and if so, subscribe to the SharedObject to be noti ed if the request is approved or denied. Example 29-2 shows how to do this by requesting space up front with the minDiskSpace parameter of flush().

Hellman s cryptanalytic TMTO does not rely on any particular properties of the underlying block cipher. But for the attack to be worth the effort, the keyspace must be small enough that the TMTO has a reasonable chance of success for a feasible pre-computation. Hellman s TMTO attack can be applied to any block cipher, provided there is sufficient computing power available for the initial pre-computation and enough storage to effectively deal with the tabulated results. Perhaps the most interesting aspect of this TMTO attack is that it requires no knowledge of the internal workings of the underlying block cipher. In the remaining section of this chapter, we analyze attacks on three specific block ciphers, namely, CMEA, Akelarre, and FEAL. Each of these attacks depends heavily on the details of the underlying algorithms and it is therefore necessary to dig into the inner workings of these ciphers. While each of these ciphers is relatively weak, the attack methods differ considerably.

EXAMPLE 29-2

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