Research Article
Grid Adaptive Bucketing Algorithm Based on Differential Privacy
| Input: Dataset D, privacy budget ε | | Output: Adversarial sample set | (1) | N = count (D) | (2) | Vm1m1 = split(D, m1) | (3) | for(i = 0; i < m1m1; i++)do | (4) | c´(Vi) = c(Vi) + Lap(1/0.5 ) | (5) | If m2 ≤ 1 then | (6) | Cbucket(c(Vi), c´(Vi)); | (7) | else | (8) | Vm2m2 = split(D, m2) | (9) | for(j = 0; j < m2m2; j++)do | (10) | c´(Vij) = c(Vij) + Lap(1/0.5) | (11) | Cbucket(c(Vij), c´(Vij)) | (12) | end for | (13) | end if | (14) | end for | (15) | AddNoise(D) | (16) | return D′ |
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