Privacy information protection method based on K-means clustering

Abstract

The invention discloses a privacy information protection method based on K-means clustering, and the method comprises the steps: carrying out the data encryption of a plurality of clients CUi, obtaining an encrypted ciphertext, and uploading the encrypted ciphertext to a cloud server; the client CUi, the cloud server CSP and the auxiliary cloud server ACSP performing data interaction calculation to obtain a re-encryption key; the cloud server receiving the encrypted ciphertext, re-encrypting the encrypted ciphertext to obtain a re-encrypted ciphertext of the same public key, and converting there-encrypted ciphertext into a Pillier encrypted ciphertext; the cloud server side obtaining the encrypted ciphertext of the Paillier and then calculating clustering; and the cloud server firstly converting the ciphertext of the clustering result into the ciphertext re-encrypted by the bidirectional addition homomorphic agent, then converting the ciphertext into the ciphertext under the public key of the client, and finally returning the ciphertext of the clustering result to the client for decryption. According to the method, the ciphertext of different public keys is clustered, the clustering result under the same public key is converted into the clustering result under different public keys to be returned to the client, and meanwhile, the private data of the client and the privacy of the clustering center are protected.

Description

technical field [0001] The invention relates to the technical field of information security, in particular to a method for protecting private information based on K-means clustering. Background technique [0002] Smart mobile devices are embedded with various sensors that record data according to people's different needs. The widespread use of smart mobile devices and the wide coverage of wireless networks promote the development of participatory sensing, an application of wireless sensor networks. Social participatory sensing addresses the limitations of participatory sensing by utilizing online social networks as infrastructure. In social participatory sensing systems, multiple users use smart devices to collect a large amount of sensing data, which can be shared and analyzed through social networks. By combining all the data in the (virtual) community and using the K-means clustering algorithm to analyze the user perception data, participating users can obtain the analy...

AI Technical Summary

Problems solved by technology

In the K-means clustering scheme in social participatory sensing, there are some security and privacy issues in the outsourcing calculation between multi-users and communities: (1) How to protect the personal privacy of users; (2) How to prevent the K-means clustering process from Disclose any additional information; (3) How to ensure that the final analysis results are only known to participating users, and the characteristic information (clustering center) of the community is not known to participating users

Once the health information of the user information is leaked, it may cause the loss of the user's personal property or even endanger personal safety

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