Author:
Abdul Aziz Nur Aziana Azwani,Hussin Masnida,Salim Nur Raidah
Abstract
The growth of the digital era with diverse existing electronic platforms offers information sharing and leads to the realization of a culture of knowledge. Vast amounts of data and information can be reached anywhere at any time, fingertips away. These data are public because people are willing to share them on digital platforms like social media. It should be noted that not all information is supposed to be made public; some is supposed to be kept private or confidential. However, people always misunderstand and are misled about which data needs to be secured and which can be shared. We proposed an attribute-based data privacy classification model using a Naïve Bayesian classifier in this work. It aims to identify and classify metadata (attributes) commonly accessible on digital platforms. We classified the attributes that had been collected into three privacy classes. Each class represents a level of data privacy in terms of its risk of breach. The public (respondent) is determined according to different ages to gather their perspective on the unclassified attribute data. The input from the survey is then used in the Naïve Bayesian classifier to formulate data weights. Then, the sorted privacy data in the class is sent back to the respondent to get their agreement on the class of attributes. We compare our approach with another classifier approach. The result shows fewer conflicting reactions from the respondents to our approach. This study could make the public aware of the importance of disclosing their information on open digital platforms.
Publisher
Universiti Putra Malaysia
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference22 articles.
1. Abraham, A., Kanjamala, E. R., Thomas, E. M., & Akhila, G. P. (2019). Email security classification of imbalanced data using naive Bayes classifier. International Journal of Wireless Communications and Network Technologies, 8(3), 16-20. https://doi.org/10.30534/ijwcnt/2019/04832019
2. Algarni, A. (2019). A survey and classification of security and privacy research in smart healthcare systems. IEEE Access, 7, 101879-101894. https://doi.org/10.1109/ACCESS.2019.2930962
3. Analysis & Policy Observatory. (2020). ACSC Annual Cyber Threat Report: July 2019 to June 2020. Australian Cyber Security Centre. https://apo.org.au/node/308071 https://www.cyber.gov.au/acsc/view-all-content/advice/personal-information-and-privacy
4. Bibhu, V., Salagrama, S., Lohani, B. P., & Kushwaha, P. K. (2021). An analytical survey of user privacy on social media platform. In 2021 International Conference on Technological Advancements and Innovations (ICTAI) (pp. 173-176). IEEE Publishing. https://doi.org/10.1109/ICTAI53825.2021.9673402
5. Budiu, R., & Moran, K. (2021). How many participants for quantitative usability studies: A summary of sample-size recommendations. Nielsen Normal Group. https://www.nngroup.com/articles/summary-quant-sample-sizes/