A novel privacy protection method based on node segmentation for social networks

Wang, Zhongli; Ju, Aiyun

doi:10.1504/ijcnds.2022.123857

Cited by 1 publication

(1 citation statement)

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“…At present, these privacy protection processing technologies are roughly divided into three categories: 1) Data distortion-based (random disturbance [1][2][3] , data injection noise [4][5][6] , etc. ); 2) Data encryption (DES encryption, RSA encryption [7][8] , etc. ); 3) Restricted conditions (K-Anonymous Privacy Protection Act [9] ).…”

Section: Introductionmentioning

confidence: 99%

Differential privacy protection algorithm for large data sources based on normalized information entropy Bayesian network

Ni,

Sun

2024

J. Phys.: Conf. Ser.

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With the rapid development of the Internet, privacy protection in big data has become an important research hotspot in the field of information security. Among them, differential privacy protection is the most widely used big data privacy protection technology today. However, the existing differential privacy protection technology cannot fully and effectively deal with the problem of high-dimensional privacy big data. Although the private data publishing method based on a Bayesian network can effectively deal with the conversion problem of high-dimensional data sets to low-dimensional data sets, this method also has some defects and deficiencies. Therefore, this paper proposes a large data source differential privacy protection algorithm (NIE-PrivBayes algorithm) based on normalized information entropy Bayesian network and proposes three improvements to the original PrivBayes algorithm. First, the original data set is protected by differential privacy on the client side. Secondly, the Bayesian network is constructed by the joint probability distribution obtained by the EM algorithm. Finally, the existing Bayesian network construction algorithm is optimized in the aspects of normalized information entropy. Based on significantly reducing the computational overhead, the effectiveness and availability of publishing high-dimensional data sets are realized under the premise of differential privacy protection.

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