Preventive Audits for Data Applications Before Data Sharing in the Power IoT

TL;DR

This paper proposes a method for preventive audits in power IoT data sharing, using mutual information as a feature to prevent information leakage by adjusting data relevance before sharing.

Contribution

It introduces a novel preventive audit framework based on mutual information and probability exchange adjustment for power IoT data sharing scenarios.

Findings

Effective prevention of information leakage demonstrated in case studies.

Proposed models adapt to multivariate data sharing scenarios.

Theoretical basis supports practical implementation.

Abstract

With the increase in data volume, more types of data are being used and shared, especially in the power Internet of Things (IoT). However, the processes of data sharing may lead to unexpected information leakage because of the ubiquitous relevance among the different data, thus it is necessary for data owners to conduct preventive audits for data applications before data sharing to avoid the risk of key information leakage. Considering that the same data may play completely different roles in different application scenarios, data owners should know the expected data applications of the data buyers in advance and provide modified data that are less relevant to the private information of the data owners and more relevant to the nonprivate information that the data buyers need. In this paper, data sharing in the power IoT is regarded as the background, and the mutual information of the data and their implicit information is selected as the data feature parameter to indicate the relevance between the data and their implicit information or the ability to infer the implicit information from the data. Therefore, preventive audits should be conducted based on changes in the data feature parameters before and after data sharing. The probability exchange adjustment method is proposed as the theoretical basis of preventive audits under simplified consumption, and the corresponding optimization models are constructed and extended to more practical scenarios with multivariate characteristics. Finally, case studies are used to validate the effectiveness of the proposed preventive audits.