Abstract
The benefits of open data were realised worldwide since the past decades, and the efforts to move more data under the license of open data intensified. There was a steep rise of open data in government repositories. In our study, we point out that privacy is one of the consistent and leading barriers among others. Strong privacy laws restrict data owners from opening the data freely. In this paper, we attempted to study the applied solutions and to the best of our knowledge, we found that anonymity-preserving algorithms did a substantial job to protect privacy in the release of the structured microdata. Such anonymity-preserving algorithms argue and compete in objectivethat not only could the released anonymized data preserve privacy but also the anonymized data preserve the required level of quality. K-anonymity algorithm was the foundation of many of its successor algorithms of all privacy-preserving algorithms. l-diversity claims to add another dimension of privacy protection. Both these algorithms used together are known to provide a good balance between privacy and quality control of the dataset as a whole entity. In this research, we have used the K-anonymity algorithm and compared the results with the addon of l-diversity. We discussed the gap and reported the benefits and loss with various combinations of K and l values, taken in combination with released data quality from an analyst’s perspective. We first used dummy fictitious data to explain the general expectations and then concluded the contrast in the findings with the real data from the food technology domain. The work contradicts the general assumptions with a specific set of evaluation parameters for data quality assessment. Additionally, it is intended to argue in favour of pushing for research contributions in the field of anonymity preservation and intensify the effort for major trends of research, considering its importance and potential to benefit people.
Highlights
Open data have proved its importance in the field of research, open governance, development versus analysis, and business initiatives. e release of public open data has emerged as a critical need for the overall development of humanity as one nation
Numerous anonymity-based algorithms have been proposed till date to preserve the anonymity concerns of the data
As it is observed from the stacked plot of all anonymization strategies, put together that the k-value is the more dominant factor in reducing the anonymized data quality compared to l-value. at is, in other words, generalization deteriorates the data quality more compared to diversity
Summary
Open data have proved its importance in the field of research, open governance, development versus analysis, and business initiatives. e release of public open data has emerged as a critical need for the overall development of humanity as one nation. Researchers worldwide used open COVID-19 data to help governments and organizations like WHO enforce measures and suggest policies. The threat to individuals to whom the data refers is shown up intensely because of the fear of identity recognition or reidentification of it. This has always been a rising concern and is being criticized since long back throughout the world, not just in the COVID-19 data but in all such released data where the identity disclosure attack is possible. Researchers have been trying to find a balance between the quality of open data released and the possibility of identity revelation from attacks
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