Enhancing correlated big data privacy using differential privacy and machine learning
Abstract Data are often correlated in real-world datasets. Existing data privacy algorithms did not consider data correlation an inherent property of datasets. This data correlation caused privacy leakages that most researchers left unnoticed. Such privacy leakages are often caused by homogeneity, b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-03-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00705-8 |
| _version_ | 1827983838161141760 |
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| author | Sreemoyee Biswas Anuja Fole Nilay Khare Pragati Agrawal |
| author_facet | Sreemoyee Biswas Anuja Fole Nilay Khare Pragati Agrawal |
| author_sort | Sreemoyee Biswas |
| collection | DOAJ |
| description | Abstract Data are often correlated in real-world datasets. Existing data privacy algorithms did not consider data correlation an inherent property of datasets. This data correlation caused privacy leakages that most researchers left unnoticed. Such privacy leakages are often caused by homogeneity, background knowledge, and linkage attacks, and the probability of such attacks increases with the magnitude of correlation among data. This problem further got magnified by the large size of real-world datasets, and we refer to these large datasets as ’Big Data.’ Several researchers proposed algorithms using machine learning models, correlation analysis, and data privacy algorithms to prevent privacy leakages due to correlation in large-sized data. The current proposed work first analyses the correlation among data. We studied the Mutual Information Correlation analysis technique and the distance correlation analysis technique for data correlation analysis. We found out distance correlation analysis technique to be more accurate for high-dimensional data. It then divides the data into blocks using the correlation computed earlier and applies the differential privacy algorithm to ensure the data privacy expectations. The results are derived based upon multiple parameters such as data utility, mean average error, variation with data size, and privacy budget values. The results showed that the proposed methodology provides better data utility when compared to the works of other researchers. Also, the data privacy commitments offered by the proposed method are comparable to the other results. Thus, the proposed methodology gives a better data utility while maintaining the required data privacy commitments. |
| first_indexed | 2024-04-09T22:52:24Z |
| format | Article |
| id | doaj.art-05889df58d284fd7af03b04a775a106f |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-09T22:52:24Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-05889df58d284fd7af03b04a775a106f2023-03-22T11:34:07ZengSpringerOpenJournal of Big Data2196-11152023-03-0110112310.1186/s40537-023-00705-8Enhancing correlated big data privacy using differential privacy and machine learningSreemoyee Biswas0Anuja Fole1Nilay Khare2Pragati Agrawal3Department of Computer Science, Maulana Azad National Institute of TechnologyDepartment of Computer Science, Maulana Azad National Institute of TechnologyDepartment of Computer Science, Maulana Azad National Institute of TechnologyDepartment of Computer Science, Maulana Azad National Institute of TechnologyAbstract Data are often correlated in real-world datasets. Existing data privacy algorithms did not consider data correlation an inherent property of datasets. This data correlation caused privacy leakages that most researchers left unnoticed. Such privacy leakages are often caused by homogeneity, background knowledge, and linkage attacks, and the probability of such attacks increases with the magnitude of correlation among data. This problem further got magnified by the large size of real-world datasets, and we refer to these large datasets as ’Big Data.’ Several researchers proposed algorithms using machine learning models, correlation analysis, and data privacy algorithms to prevent privacy leakages due to correlation in large-sized data. The current proposed work first analyses the correlation among data. We studied the Mutual Information Correlation analysis technique and the distance correlation analysis technique for data correlation analysis. We found out distance correlation analysis technique to be more accurate for high-dimensional data. It then divides the data into blocks using the correlation computed earlier and applies the differential privacy algorithm to ensure the data privacy expectations. The results are derived based upon multiple parameters such as data utility, mean average error, variation with data size, and privacy budget values. The results showed that the proposed methodology provides better data utility when compared to the works of other researchers. Also, the data privacy commitments offered by the proposed method are comparable to the other results. Thus, the proposed methodology gives a better data utility while maintaining the required data privacy commitments.https://doi.org/10.1186/s40537-023-00705-8Big data privacyCorrelated datasetsData correlationMachine learningCorrelated big dataData privacy threats |
| spellingShingle | Sreemoyee Biswas Anuja Fole Nilay Khare Pragati Agrawal Enhancing correlated big data privacy using differential privacy and machine learning Journal of Big Data Big data privacy Correlated datasets Data correlation Machine learning Correlated big data Data privacy threats |
| title | Enhancing correlated big data privacy using differential privacy and machine learning |
| title_full | Enhancing correlated big data privacy using differential privacy and machine learning |
| title_fullStr | Enhancing correlated big data privacy using differential privacy and machine learning |
| title_full_unstemmed | Enhancing correlated big data privacy using differential privacy and machine learning |
| title_short | Enhancing correlated big data privacy using differential privacy and machine learning |
| title_sort | enhancing correlated big data privacy using differential privacy and machine learning |
| topic | Big data privacy Correlated datasets Data correlation Machine learning Correlated big data Data privacy threats |
| url | https://doi.org/10.1186/s40537-023-00705-8 |
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