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Spectral clustering algorithm based on differential privacy protection
ZHENG Xiaoyao, CHEN Dongmei, LIU Yuqing, YOU Hao, WANG Xiangshun, SUN Liping
Journal of Computer Applications
2018, 38 (10):
2918-2922.
DOI: 10.11772/j.issn.1001-9081.2018040888
Aiming at the problem of privacy leakage in the application of traditional clustering algorithm, a spectral clustering algorithm based on differential privacy protection was proposed. Based on the differential privacy model, the cumulative distribution function was used to generate random noise that satisfies Laplasse distribution. Then the noise was added to the sample similarity function calculated by the spectral clustering algorithm, which disturbed the weight values between the individual samples and realized information hiding between sample individuals for privacy protection. Experimental results of UCI dataset verify that the proposed algorithm can achieve effective data clustering within a certain degree of information loss, and can also protect clustered data.
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