A power transaction big data publishing method based on differential privacy protection

Abstract

The invention discloses a power transaction big data publishing method based on differential privacy protection, which comprises the following steps of utilizing MICFS to carry out feature selection on the correlation of the original data set of the power transaction, and selecting the data record with low correlation to generate the data set B to be determined; using a clustering algorithm to partition a K-block to obtain a plurality of sub-data blocks with mutually independent attributes; after deleting a record in the sub-data block, using the inquiry function f to inquire about the sensitivity GSD of the original data set and the sensitivity GSB of the inquiry B; according to the property of differential privacy parallel combination, adding Laplace noise, obtaining the training samplequery set satisfying the differential privacy as a set of machine learning training samples; training the Lasso regression algorithm to generate the prediction model, and inputting the original data set into the model, and outputting the query set of D. The method of the invention enables the data publishing accuracy and safety to be improved, and reduce the computing overhead and privacy budgets.

Description

technical field [0001] The invention relates to the field of information technology security, in particular to a method for publishing big data of power trading based on differential privacy protection. Background technique [0002] With the development of smart grid and big data technology, the data sharing mode of data mining and analysis of the state of the power industry by using the released power system big data has become the development trend of the electric power information age. However, while data sharing brings convenience, it is also accompanied by the risk of personal privacy data leakage, so the release of privacy-protected data has attracted widespread attention. [0003] Traditional privacy-preserving data release models, such as k-anonymity, l-diversity, t-approximation and other models, they generalize all data records into several groups of records according to the characteristics of the original data, not only making each group in each group Records can...

AI Technical Summary

Problems solved by technology

[0010] The technical problem to be solved by the present invention is the problem that the existing differential privacy technology cannot provide accurate query results when querying and publishing data due to the high correlation between a large number of query sets in non-interactive data protection application scenarios

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