Index file section Math for vldbe.bib
Last update: Sun Sep 1 02:39:25 MDT 2024
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Math
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$ + + $, 5(7)622--633
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$-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however, are either limited to non-standard types of regression or unable to produce accurate regression results. Motivated by this, we propose the Functional Mechanism, a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce $,
5(11)1364--1375
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$-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce $,
5(11)1364--1375
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$^{0.364}$, 5(11)1507--1518
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$^{0.520}$, 5(11)1507--1518
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$^{0.723}$, 5(11)1507--1518
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$^{0.896}$, 5(11)1507--1518
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$ < 1$, 4(3)173--184
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$_1$, 5(10)908--919
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$ 2 (1 + \epsilon) $, 5(5)454--465
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$3$, 3(1)3--3
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$ \beta $, 5(11)1388--1399
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$ \epsilon $, 2(1)169--180, 4(3)173--184
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$ \epsilon > 0 $, 5(5)454--465
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$ (\epsilon, \delta)$, 5(6)514--525
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$_i$, 5(10)908--919
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$ k = \infty $, 5(11)1292--1303
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$l$, 5(3)229--240
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$m$, 4(3)173--184, 5(11)1507--1518
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$_n$, 5(10)908--919
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$n$, 4(3)173--184, 5(10)1052--1063, 5(11)1507--1518
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$ O(k / (R^{(1 - \alpha) / \alpha }))$, 4(3)173--184
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$ O(\log_{1 + \epsilon } n) $, 5(5)454--465
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$ O(m n) $, 5(11)1507--1518
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$ O(m n / \epsilon)$, 4(3)173--184
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$ \Omega (m^2 / \ln (1 / (1 - \epsilon)))$, 4(3)173--184
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$ O(n \ln m / \epsilon^2)$, 4(3)173--184
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$ O(n^2) $, 5(10)1052--1063
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$ O(n^{3 / 2})$, 5(10)1052--1063
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$_q$, 5(10)908--919
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$q$, 4(3)173--184
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$r$, 4(10)681--692
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$ R > q \ln n$, 4(3)173--184
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$t$, 5(10)1052--1063, 5(11)1412--1423
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$ \tau $, 5(11)1400--1411
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$ \theta $, 5(11)1340--1351
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$^x$, 5(9)860--871