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

A technology of differential privacy and power trading, which is applied in the field of information technology security, can solve problems such as unavailability of data, and achieve the effects of protecting against leakage, reducing computing overhead, and satisfying data protection

Active Publication Date: 2019-02-22
广州电力交易中心有限责任公司 +1
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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

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

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Embodiment Construction

[0049]The present invention proposes a power transaction big data release method based on differential privacy protection, aiming at ensuring the security of personal sensitive data in power transaction big data and the availability of published data under the framework of non-interactive differential privacy.

[0050] In order to solve the problem that the existing differential privacy technology cannot provide accurate query results when querying the published data due to the high correlation between a large number of query sets in the application scenario of non-interactive data protection, the present invention does not consider that the attacker has In the case of what kind of background knowledge, use machine learning and differential privacy technology to realize privacy-protected data release, effectively select low-correlation query data sets as training samples, and use Lasso regression algorithm to train samples to generate prediction models; then Utilize predictive ...

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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...

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Application Information

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IPC IPC(8): G06F21/62G06F16/9032
CPCG06F21/6245
Inventor 杜龙
Owner 广州电力交易中心有限责任公司
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