Preventive Audits to Protect Private Information and Enhance Utility in Power IoT Data Sharing

Core Concepts

Preventive audits should be conducted before data sharing in power IoT to avoid unexpected information leakage by adjusting the mutual information between unshared data and private/nonprivate characteristics of data owners.

Abstract

The paper discusses the need for preventive audits before data sharing in the power Internet of Things (IoT) to avoid unexpected information leakage. As data volume and sharing increase, the processes of data sharing may lead to unintended disclosure of private information of data owners due to the ubiquitous relevance among different data.
The key aspects are:

Mutual information is used as the data feature parameter to indicate the relevance between data and private/nonprivate characteristics of data owners.

A probability exchange adjustment method is proposed as the theoretical basis for preventive audits, which inversely adjusts the mutual information pair between unshared data and private/nonprivate characteristics to meet the privacy requirements of data owners and utility requirements of data service providers.

Optimization models are constructed and extended to more practical scenarios with multivariate private and nonprivate characteristics.

Case studies validate the effectiveness of the proposed preventive audits in protecting private information and enhancing utility for data sharing in power IoT.

Stats

The volume of data created, captured, copied, and consumed worldwide may increase to more than 180 zettabytes by 2025, which is ten times larger than the value in 2015.

Quotes

"To prevent data service providers from using data inappropriately, which may harm the legitimate rights and interests of data owners in protecting their data privacy, it is necessary to conduct comprehensive preventive audits for data applications before sharing data."
"Preventive audits should analyze the correlation between the unshared data and the private characteristics of the data owners, and preprocess the unshared data to minimize the correlation."

Key Insights Distilled From

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

by Bohong Wang,... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02963.pdf

Deeper Inquiries

How can the proposed preventive audit framework be extended to handle continuous random variables for private and nonprivate characteristics

To extend the proposed preventive audit framework to handle continuous random variables for private and nonprivate characteristics, we can utilize techniques from probability theory and information theory. One approach is to discretize the continuous random variables into intervals or bins, similar to how the discrete random variables were handled in the framework. By discretizing the continuous variables, we can still calculate the conditional information entropy and mutual information, albeit with a larger number of intervals to represent the continuous range of values. This discretization allows us to apply the same principles of adjusting probability distributions to achieve the desired privacy-utility trade-off. Additionally, we can use techniques like kernel density estimation or Gaussian mixture models to approximate the continuous distributions and perform the necessary calculations for the preventive audits.

What are the potential challenges in implementing the preventive audits in real-world power IoT data sharing scenarios with multiple stakeholders

Implementing preventive audits in real-world power IoT data sharing scenarios with multiple stakeholders may face several challenges. One challenge is the complexity of data ownership and access rights in a multi-stakeholder environment. Different stakeholders may have varying levels of control over the data and different privacy concerns, making it challenging to establish a unified preventive audit framework that satisfies all parties. Additionally, ensuring compliance with data protection regulations and standards across multiple stakeholders can be a challenge, especially when data sharing involves sensitive information. Another challenge is the scalability of the preventive audit process, especially when dealing with large volumes of data from multiple sources. Ensuring the efficiency and effectiveness of the audits while handling diverse data sources and formats can be a significant challenge in real-world implementations.

How can the preventive audit process be further automated and integrated into the data sharing workflow to enable efficient and scalable data protection

To further automate and integrate the preventive audit process into the data sharing workflow, several steps can be taken. Firstly, developing automated algorithms or tools that can analyze data characteristics, identify potential privacy risks, and suggest adjustments to the data before sharing can streamline the audit process. Machine learning algorithms can be employed to learn patterns in data and predict the impact of sharing on privacy and utility. Secondly, integrating the preventive audit process into data sharing platforms or systems used by stakeholders can ensure that audits are conducted seamlessly as part of the data sharing workflow. This integration can include automated notifications, alerts, and recommendations for data adjustments based on the audit results. Lastly, establishing clear protocols and guidelines for conducting preventive audits, including defining roles and responsibilities of stakeholders in the audit process, can help ensure consistency and efficiency in implementing the audits across different data sharing scenarios.