Incremental <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized Scheduling
Improvements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8491270/ |
| _version_ | 1818874788378050560 |
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| author | David Rebollo-Monedero Cesar Hernandez-Baigorri Jordi Forne Miguel Soriano |
| author_facet | David Rebollo-Monedero Cesar Hernandez-Baigorri Jordi Forne Miguel Soriano |
| author_sort | David Rebollo-Monedero |
| collection | DOAJ |
| description | Improvements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of our privacy. The k-anonymous microaggregation permits releasing a dataset where each person remains indistinguishable from other k -1 individuals, through the aggregation of demographic attributes, otherwise a potential culprit for respondent reidentification. Although privacy guarantees are by no means absolute, the elegant simplicity of the k-anonymity criterion and the excellent preservation of information utility of microaggregation algorithms has turned them into widely popular approaches whenever data utility is critical. Unfortunately, high-utility algorithms on large datasets inherently require extensive computation. This paper addresses the need of running k-anonymous microaggregation efficiently with mild distortion loss, exploiting the fact that the data may arrive over an extended period of time. Specifically, we propose to split the original dataset into two portions that will be processed subsequently, allowing the first process to start before the entire dataset is received while leveraging the superlinearity of the involved microaggregation algorithms. A detailed mathematical formulation enables us to calculate the optimal time for the fastest anonymization as well as for minimum distortion under a given deadline. Two incremental microaggregation algorithms are devised, for which extensive experimentation is reported. The presented theoretical methodology should prove invaluable in numerous data-collection applications, including large-scale electronic surveys in which computation is possible as the data come in. |
| first_indexed | 2024-12-19T13:16:10Z |
| format | Article |
| id | doaj.art-c24b933268fb47be9fb4ed947c92544d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:16:10Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c24b933268fb47be9fb4ed947c92544d2022-12-21T20:19:49ZengIEEEIEEE Access2169-35362018-01-016600166004410.1109/ACCESS.2018.28759498491270Incremental <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized SchedulingDavid Rebollo-Monedero0https://orcid.org/0000-0002-0783-2382Cesar Hernandez-Baigorri1Jordi Forne2Miguel Soriano3Department of Telematic Engineering, Universitat Politècnica de Catalunya, Barcelona, SpainDepartment of Telematic Engineering, Universitat Politècnica de Catalunya, Barcelona, SpainDepartment of Telematic Engineering, Universitat Politècnica de Catalunya, Barcelona, SpainDepartment of Telematic Engineering, Universitat Politècnica de Catalunya, Barcelona, SpainImprovements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of our privacy. The k-anonymous microaggregation permits releasing a dataset where each person remains indistinguishable from other k -1 individuals, through the aggregation of demographic attributes, otherwise a potential culprit for respondent reidentification. Although privacy guarantees are by no means absolute, the elegant simplicity of the k-anonymity criterion and the excellent preservation of information utility of microaggregation algorithms has turned them into widely popular approaches whenever data utility is critical. Unfortunately, high-utility algorithms on large datasets inherently require extensive computation. This paper addresses the need of running k-anonymous microaggregation efficiently with mild distortion loss, exploiting the fact that the data may arrive over an extended period of time. Specifically, we propose to split the original dataset into two portions that will be processed subsequently, allowing the first process to start before the entire dataset is received while leveraging the superlinearity of the involved microaggregation algorithms. A detailed mathematical formulation enables us to calculate the optimal time for the fastest anonymization as well as for minimum distortion under a given deadline. Two incremental microaggregation algorithms are devised, for which extensive experimentation is reported. The presented theoretical methodology should prove invaluable in numerous data-collection applications, including large-scale electronic surveys in which computation is possible as the data come in.https://ieeexplore.ieee.org/document/8491270/Data privacystatistical disclosure control<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-anonymitymicroaggregationelectronic surveyslarge-scale datasets |
| spellingShingle | David Rebollo-Monedero Cesar Hernandez-Baigorri Jordi Forne Miguel Soriano Incremental <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized Scheduling IEEE Access Data privacy statistical disclosure control <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-anonymity microaggregation electronic surveys large-scale datasets |
| title | Incremental <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized Scheduling |
| title_full | Incremental <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized Scheduling |
| title_fullStr | Incremental <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized Scheduling |
| title_full_unstemmed | Incremental <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized Scheduling |
| title_short | Incremental <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized Scheduling |
| title_sort | incremental inline formula tex math notation latex k tex math inline formula anonymous microaggregation in large scale electronic surveys with optimized scheduling |
| topic | Data privacy statistical disclosure control <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-anonymity microaggregation electronic surveys large-scale datasets |
| url | https://ieeexplore.ieee.org/document/8491270/ |
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