Contents.Versions and key features FileVault was introduced with (10.3), and could only be applied to a user's home directory, not the startup volume. The uses an encrypted (a large single file) to present a volume for the home directory. And use more modern which spread the data over 8 MB files (called bands) within a bundle.

Apple refers to this original iteration of FileVault as legacy FileVault.(2011) and newer offer FileVault 2, which is a significant redesign. This encrypts the entire OS X startup volume and typically includes the home directory, abandoning the disk image approach. For this approach to, authorised users' information is loaded from a separate non-encrypted boot volume (partition/slice type AppleBoot).FileVault The original version of FileVault was added in Mac OS X Panther to encrypt a user's home directory.Master passwords and recovery keys When FileVault is enabled the system invites the user to create a master password for the computer. Apple Press Info. June 23, 2003. Retrieved January 21, 2013.

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The rate-reliability-complexity limits of a quasi-static K-user multiple access channel (MAC), with or without feedback, are explored in this paper. Using Performance-Complexity Analysis for MAC ML-Based Decoding With User Selection - IEEE Journals & Magazine. Operations has a worst-case time complexity of O(m lg n) without guaranteeing than each single operation has O(l g n) t ime complexity. Weiss covers this scheme.6 5 Mark Allen Weiss, Data Structures and Algorithm Analysis in Java, Addison-Wesley, 1999, pages 118-138. 6 Ibid, pages pages 118-138.

(February 2008). Archived from (PDF) on May 14, 2008. Cite journal requires journal=. (PDF). ^ Apple, Inc (August 17, 2012).

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Abstract: This work explores the rate-reliability-complexity limits of the quasi-staticK-user multiple access channel (MAC), with or without feedback. Using high-SNRasymptotics, the work first derives bounds on the computational resourcesrequired to achieve near-optimal (ML-based) decoding performance. It thenbounds the (reduced) complexity needed to achieve any (including suboptimal)diversity-multiplexing performance tradeoff (DMT) performance, and finallybounds the same complexity, in the presence of feedback-aided user selection.This latter effort reveals the ability of a few bits of feedback not only toimprove performance, but also to reduce complexity. In this context, theanalysis reveals the interesting finding that proper calibration of userselection can allow for near-optimal ML-based decoding, with complexity thatneed not scale exponentially in the total number of codeword bits. The derivedbounds constitute the best known performance-vs-complexity behavior to date forML-based MAC decoding, as well as a first exploration of thecomplexity-feedback-performance interdependencies in multiuser settings.

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From: Hsiao-feng Lu [view email]
[v1] Thu, 28 May 2015 15:23:35 UTC (71 KB)
[v2]Tue, 2 Jun 2015 15:43:05 UTC (81 KB)
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