A password-based key derivation function (KDF)-a function that derives cryptographic keys from a password-is necessary in many security applications. Like any password-based schemes, such KDFs are subject to key search attacks (often called dictionary attacks). Salt and iteration count are used in practice to significantly increase the workload of such attacks. These techniques have also been specified in widely adopted industry standards such as PKCS and IETF. Despite the importance and widespread usage, there has been no formal security analysis on existing constructions. In this correspondence, we propose a general security framework for password-based KDFs and introduce two security definitions each capturing a different attacking scenario. We study the most commonly used construction H/sup (c)/(p/spl par/s) and prove that the iteration count c, when fixed, does have an effect of stretching the password p by log/sub 2/c bits. We then analyze the two standardized KDFs in PKCS #5. We show that both are secure if the adversary cannot influence the parameters but subject to attacks otherwise. Finally, we propose a new password-based KDF that is provably secure even when the adversary has full control of the parameters.

We explore the power of teaChillg by StUdyiIlg two uu-lille learuing models: teach er-clirecteci learninE and self-dlrectecl learning. In both models, the learner tries to identify an unkuowu concept based on examples of the concept presented one at, a time. The learner predirts wheth~r each example is positive or negative with immediate feedback, and the ol)ject,ive is to minimize the uurnl)er of predict,iou mistakes. ThP examples are selected by the teacher in teacher-dlrectecl learning and hy tlhe learner itself in self-directed learning. R,oughly, teacher-directed learning represents the scenario in which a teacher teaches a class of learners, and self-directed learning represents the scenario in which a smart learnerasks questious and learns by itself. For all previolmly studied concept classes, the rnirrimum numl)er of mistalws in teacller-ciirectf ecl learning is always larger than that, in self-directed learning. This raises an mtermting question [.)t ’ whrt, hrr teaching is helpful for all learners mrlu(ling the smart learner’. Assuming the existence of clue-way functioms, we construct com cept clahses for which the miuimum nurnher of mislakes is hnear in teacher-directed learning I,ut sllI>rrlJolyllorlllal m self-directed learning, cler~lc~llst,rt~tillg the power of a helpful teacher in a Iearmng process.

Article Reducing the number of queries in self-directed learning Share on Author: Yiqun Lisa Yin RSA Laboratories, 100 Marine Parkway, Redwood City, CA RSA Laboratories, 100 Marine Parkway, Redwood City, CAView Profile Authors Info & Claims COLT '95: Proceedings of the eighth annual conference on Computational learning theoryJuly 1995 Pages 128–135https://doi.org/10.1145/225298.225313Online:05 July 1995Publication History 1citation185DownloadsMetricsTotal Citations1Total Downloads185Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access