Fig. 1 | Scientific Reports

Fig. 1

From: Application of the joint clustering algorithm based on Gaussian kernels and differential privacy in lung cancer identification

Fig. 1

The impact of Laplace noise settings with different parameters on image information. (A) The original lung CT image. (B) Laplacian noise distribution with different parameter settings. (C) The original lung cancer image after adding Laplacian noise with parameters \(\:\mu\:=0,\epsilon\:=0.5\),\(\:\varDelta\:f=0.05\:and\:b=0.1\). (D) The original lung cancer image after adding Laplacian noise with parameters \(\:\mu\:=0,\epsilon\:=0.2\),\(\:\varDelta\:f=0.05\:and\:b=0.25\). (E) The original lung cancer image after adding Laplacian noise with parameters \(\:\mu\:=0,\epsilon\:=0.5\),\(\:\varDelta\:f=0.125\:and\:b=0.25.\) (F) The original lung cancer image after adding Laplacian noise with parameters \(\:\mu\:=0.5,\epsilon\:=0.5\),\(\:\varDelta\:f=0.05\:and\:b=0.1.\).

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