Performance under different protocols. (a) Online dataset, NCR (normalized cumulative rank), k = 64. (b) Online dataset, squared error, k = 64. (c) IBM dataset, NCR, k = 64. (d) IBM dataset, squared error, k = 64.

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Performance under different protocols. (a) Online dataset, NCR (normalized cumulative rank), k = 64. (b) Online dataset, squared error, k = 64. (c) IBM dataset, NCR, k = 64. (d) IBM dataset, squared error, k = 64.

2023, 38(6): 1403-1422.

Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy .

Figures of the Article

Innovations: sacrificing non-sensitive privacy for more accurate results.
Illustration of UISM.
Illustration of encoding and perturbation in IHFO. (a) Process of IHFO. (b) Mapping Hadamard matrix.
Performance under different protocols. (a) Online dataset, NCR (normalized cumulative rank), k = 64. (b) Online dataset, squared error, k = 64. (c) IBM dataset, NCR, k = 64. (d) IBM dataset, squared error, k = 64.
Singleton identification for the IBM dataset. (a) IBM dataset, NCR (normalized cumulative rank), k = 64. (b) IBM dataset, SE (squared error), k = 64. (c) IBM dataset, KLD (Kullback-Leibler divergence), k = 64. (d) IBM dataset, NCR, k = 32. (e) IBM dataset, SE, k = 32. (f) IBM dataset, KLD, k = 32.
Singleton identification for the Online dataset. (a) Online dataset, NCR, k = 64. (b) Online dataset, SE, k = 64. (c) Online dataset, KLD, k = 64. (d) Online dataset, NCR, k = 32. (e) Online dataset, SE, k = 32. (f) Online dataset, KLD, k = 32.
Itemset mining results for the IBM dataset. (a) IBM dataset, NCR, k = 64. (b) IBM dataset, SE, k = 64. (c) IBM dataset, KLD, k = 64. (d) IBM dataset, NCR, k = 32. (e) IBM dataset, SE, k = 32. (f) IBM dataset, KLD, k = 32.
Itemset mining results for the Online dataset. (a) Online dataset, NCR, k = 64. (b) Online dataset, SE, k = 64. (c) Online dataset, KLD, k = 64. (d) Online dataset, NCR, k = 32. (e) Online dataset, SE, k = 32. (f) Online dataset, KLD, k = 32.
(a) NCR and (b) SE of varying dataset division $\eta$ under different $\varepsilon$ .

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