Privacy Threats and Privacy Preservation Techniques for Farmer Data Collections Based on Data Shuffling

Abstract

Aside from smart technologies, farm data collection is also important for smart farms including farm environment data collection and farmer survey data collection. With farm data collection, we observe that it is generally proposed to utilize in smart farm systems. However, it can also be released for use in the outside scope of the data collecting organization for an appropriate business reason such as improving the smart farm system, product quality, and customer service. Moreover, we can observe that the farmer survey data collection often includes sensitive data, the private data of farmers. Thus, it could lead to privacy violation issues when it is released. To address these issues in the farmer survey data collection, an anatomization model can protect the users' private data that is available in farmer survey data collection to be proposed. However, it still has disorganized issues and privacy violation issues in the sensitive table that must be addressed. To rid these vulnerabilities of anatomization models, a new privacy preservation model based on data shuffing is proposed in this work. Moreover, the proposed model is evaluated by conducting extensive experiments. The experimental results indicate that the proposed model is more efficient than the anatomization model for the farmer survey data collection. That is, the adversary can have the confidence for re-identifying every sensitive data that is available in farmer survey data collection that is after satisfied by the privacy preservation constraint of the proposed model to be at most 1/l. Furthermore, after the farmer survey data collection satisfies the privacy preservation constraint of the proposed model, it does not have disorganized issues and privacy violation issues from considering the sensitive values.