  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.
