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Implementation of multiplicative noise model ■ a random fuzz factor δ j is drawn for each establishment j p (δ j ) = F (δ j ) = ⎧ ⎪⎨ ⎪⎩ ⎧ ⎪⎨ ⎪⎩ (b − δ)/(b − a) 2 , δ ∈ [a, b] (b + δ − 2)/(b − a) 2 , δ ∈ [2 − b, 2 − a] 0, otherwise [ 0, δ < 2 − b (δ + b − 2) 2 / 2 (b − a) 2] , δ ∈ [2 − b, 2 − a] [ 0.5, δ ∈ (2 − a, a) 0.5 + (b − a) 2 − (b − δ) 2] , δ ∈ [a, b] 1, δ > b where a = 1 + c/100 and b = 1 + d/100 are constants chosen such that the true value is distorted by a minimum of c percent and a maximum of d percent. August 10, 2005 - p. 9/31
Distribution of Fuzz Factors 0 0 2−b 2−a 1 a b 2 August 10, 2005 - p. 10/31
Page 1 and 2: GoBack
Page 3 and 4: Introduction ■ We will describe t
Page 5 and 6: Protection provided ■ In general,
Page 7 and 8: Three-layer confidentiality protect
Page 9: Three-layer confidentiality protect
Page 13 and 14: Distorting ratios Ratios are distor
Page 15 and 16: Item suppression ■ Some disclosur
Page 17 and 18: Protection by Weighting ■ QCEW 1,
Page 19 and 20: Protection Table 1: Small Cells: B,
Page 21 and 22: Protection Table 3: Small Cells: B,
Page 23 and 24: Distribution of the Error in the Fi
Page 29 and 30: Cross-sectional unbiasedness: B Fra
Page 31 and 32: Conclusion ■ First large-scale im

presentation - VirtualRDC @ Cornell - Cornell University

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