Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation
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 barrier...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2021/9929049 |
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| author | Shah Imran Alam Ihtiram Raza Khan Syed Imtiyaz Hassan Farheen Siddiqui M. Afshar Alam Anil Kumar Mahto |
| author_facet | Shah Imran Alam Ihtiram Raza Khan Syed Imtiyaz Hassan Farheen Siddiqui M. Afshar Alam Anil Kumar Mahto |
| author_sort | Shah Imran Alam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e2cf0dc1c695420a9aa77d9ff9400834 |
| institution | Kabale University |
| issn | 0146-9428 1745-4557 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-e2cf0dc1c695420a9aa77d9ff94008342025-02-03T05:45:57ZengWileyJournal of Food Quality0146-94281745-45572021-01-01202110.1155/2021/99290499929049Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled PerturbationShah Imran Alam0Ihtiram Raza Khan1Syed Imtiyaz Hassan2Farheen Siddiqui3M. Afshar Alam4Anil Kumar Mahto5Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, IndiaDepartment of Computer Science and Engineering, Jamia Hamdard, New Delhi, IndiaDepartment of Computer Science and Information Technology, School of Technology, Maulana Azad National Urdu University, Hyderabad, IndiaDepartment of Computer Science and Engineering, Jamia Hamdard, New Delhi, IndiaDepartment of Computer Science and Engineering, Jamia Hamdard, New Delhi, IndiaDepartment of Computer Science and Engineering, Jamia Hamdard, New Delhi, IndiaThe 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.http://dx.doi.org/10.1155/2021/9929049 |
| spellingShingle | Shah Imran Alam Ihtiram Raza Khan Syed Imtiyaz Hassan Farheen Siddiqui M. Afshar Alam Anil Kumar Mahto Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation Journal of Food Quality |
| title | Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation |
| title_full | Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation |
| title_fullStr | Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation |
| title_full_unstemmed | Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation |
| title_short | Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation |
| title_sort | open data release and privacy concerns complexity in mitigating vulnerability with controlled perturbation |
| url | http://dx.doi.org/10.1155/2021/9929049 |
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