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An Anatomization Model for Farmer Data Collections

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Abstract

Smart farming is one of the big concepts that are regarding in the world. It is a development technique for modern agricultures. It generally emphasizes the use of data sensors and data communication technologies in conjunction with an appropriate data analysis technique. Aside from data sensors and data communication technologies, survey questionnaires can also be used to collect the data for developing the smart farming. With survey questionnaires, they are generally proposed to collect the answer about the farmer’s opinion to the specified questions and his/her history data. For this reason, the data sets of smart farming are often the high dimension of quasi-identifier and more sensitive attributes. Moreover, the sensitive attributes of farmer data sets are multiple groups. Thus, privacy preservation models could be insufficient to address privacy violation issues in farmer data sets when they are released for public use. For this reason, an appropriate privacy preservation model for farmer data sets is proposed in this work. Furthermore, the proposed model is evaluated by using extensive experiments. From the results, we found that the proposed model is highly effective than the compared privacy preservation models.

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Authors and Affiliations

  1. Maejo University (MJU), Chiangmai-Phrao Road, Maejo, Sansai, Chiang Mai, 50290, Thailand

    Surapon Riyana, Noppamas Riyana & Warunsiri Sujinda

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  1. Surapon Riyana
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  2. Noppamas Riyana
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  3. Warunsiri Sujinda
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Correspondence to Surapon Riyana.

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This article is part of the topical collection “Security and Privacy 2020” guest edited by Pantelimon Stanica, Odelu Vanga and Sumit Kumar Debnath.

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Riyana, S., Riyana, N. & Sujinda, W. An Anatomization Model for Farmer Data Collections. SN COMPUT. SCI. 2, 353 (2021). https://doi.org/10.1007/s42979-021-00740-3

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