A privacy enhancing model for Internet of Things using three-way decisions and differential privacy

The recent advancements in Internet of Things (IoT) have brought enormous advantages for businesses. These benefits are achieved by services that collect large volumes of data that is collected for analysis. The data may also contain sensitive information. Privacy of such data is an important research challenge. Differential privacy is a recent technique for data privacy. It works by anonymizing the attributes that may contain sensitive information. An essential step before applying differential privacy is the division of attribute set into three groups called sensitive, non-sensitive and ambiguous. A key issue in existing studies is that the division of attribute set is done manually by a domain expert and is therefore costly. We introduce a three-way approach for differential privacy and a supporting algorithm for this demarcation of attribute sets. Results indicate that the information content and stability of the dataset improves considerably with our approach.

Automated summary

The recent advancements in IoT have brought significant advantages for businesses, but the protection of data privacy has become an important research challenge. Differential privacy, a new technique, anonymizes sensitive attributes to protect data privacy. A key issue in existing studies is the costly manual division of attribute sets by domain experts. This paper introduces a three-way approach for differential privacy and an algorithm for attribute set division, demonstrating considerable improvement in information content and dataset stability.