Differentially private data publishing via cross-moment microaggregation
Differential privacy is one of the most prominent privacy notions in the field of anonymization. However, its strong privacy guarantees very often come at the expense of significantly degrading the utility of the protected data. To cope with this, numerous mechanisms have been studied that reduce th...
| Authors: | ; ; |
|---|---|
| Format: | Article |
| Publication date: | 2020 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/386400 |
| Online Access: | https://hdl.handle.net/2117/386400 https://dx.doi.org/10.1016/j.inffus.2019.06.011 |
| Access Level: | Open access |
| Keyword: | Data protection Data privacy Microaggregation Differential privacy Data utility Protecció de dades Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| _version_ | 1878738666878140416 |
|---|---|
| author | Parra Arnau, Javier|||0000-0002-1772-1088 |
| author2 | Domingo Ferrer, Josep Soria Comas, Jordi |
| author2_role | author author |
| author_browse | Domingo Ferrer, Josep Parra Arnau, Javier|||0000-0002-1772-1088 Soria Comas, Jordi |
| author_facet | Parra Arnau, Javier|||0000-0002-1772-1088 Domingo Ferrer, Josep Soria Comas, Jordi |
| author_role | author |
| collection | UPCommons. Portal del coneixement obert de la UPC |
| description | Differential privacy is one of the most prominent privacy notions in the field of anonymization. However, its strong privacy guarantees very often come at the expense of significantly degrading the utility of the protected data. To cope with this, numerous mechanisms have been studied that reduce the sensitivity of the data and hence the noise required to satisfy this notion. In this paper, we present a generalization of classical microaggregation, where the aggregated records are replaced by the group mean and additional statistical measures, with the purpose of evaluating it as a sensitivity reduction mechanism. We propose an anonymization methodology for numerical microdata in which the target of protection is a data set microaggregated in this generalized way, and the disclosure risk limitation is guaranteed through differential privacy via record-level perturbation. Specifically, we describe three anonymization algorithms where microaggregation can be applied to either entire records or groups of attributes independently. Our theoretical analysis computes the sensitivities of the first two central cross moments; we apply fundamental results from matrix perturbation theory to derive sensitivity bounds on the eigenvalues and eigenvectors of the covariance and coskewness matrices. Our extensive experimental evaluation shows that data utility can be enhanced significantly for medium to large sizes of the microaggregation groups. For this range of group sizes, we find experimental evidence that our approach can provide not only higher utility but also higher privacy than traditional microaggregation. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_b2edd06b13c4ccbcf4e31ee460af2ea1 |
| institution | Universitat Politècnica de Catalunya (UPC) |
| instname_str | Universitat Politècnica de Catalunya (UPC) |
| language | eng |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:upcommons.upc.edu:2117/386400 |
| publishDate | 2020 |
| publishDateSort | 2020 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | UPCommons. Portal del coneixement obert de la UPC |
| spelling | Differentially private data publishing via cross-moment microaggregation Parra Arnau, Javier|||0000-0002-1772-1088 Domingo Ferrer, Josep Soria Comas, Jordi Data protection Data privacy Microaggregation Differential privacy Data utility Protecció de dades Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors Differential privacy is one of the most prominent privacy notions in the field of anonymization. However, its strong privacy guarantees very often come at the expense of significantly degrading the utility of the protected data. To cope with this, numerous mechanisms have been studied that reduce the sensitivity of the data and hence the noise required to satisfy this notion. In this paper, we present a generalization of classical microaggregation, where the aggregated records are replaced by the group mean and additional statistical measures, with the purpose of evaluating it as a sensitivity reduction mechanism. We propose an anonymization methodology for numerical microdata in which the target of protection is a data set microaggregated in this generalized way, and the disclosure risk limitation is guaranteed through differential privacy via record-level perturbation. Specifically, we describe three anonymization algorithms where microaggregation can be applied to either entire records or groups of attributes independently. Our theoretical analysis computes the sensitivities of the first two central cross moments; we apply fundamental results from matrix perturbation theory to derive sensitivity bounds on the eigenvalues and eigenvectors of the covariance and coskewness matrices. Our extensive experimental evaluation shows that data utility can be enhanced significantly for medium to large sizes of the microaggregation groups. For this range of group sizes, we find experimental evidence that our approach can provide not only higher utility but also higher privacy than traditional microaggregation. The authors are thankful to A. Azzalini for his clarifications on the sampling of multivariate skew-normal distributions. Partial support to this work has been received from the European Commission (projects H2020-644024 “CLARUS” and H2020-700540 “CANVAS”), the Government of Catalonia (ICREA Academia Prize to J. Domingo-Ferrer), and the Spanish Government (projects TIN2014-57364-C2-1-R “Smart-Glacis” and TIN2016-80250-R “Sec-MCloud”). J. Parra-Arnau is the recipient of a Juan de la Cierva postdoctoral fellowship, FJCI-2014-19703, from the Spanish Ministry of Economy and Competitiveness. The authors are with the UNESCO Chair in Data Privacy, but the views in this paper are their own and are not necessarily shared by UNESCO. Elsevier https://hdl.handle.net/2117/386400 https://dx.doi.org/10.1016/j.inffus.2019.06.011 |
| spellingShingle | Differentially private data publishing via cross-moment microaggregation Parra Arnau, Javier|||0000-0002-1772-1088 Data protection Data privacy Microaggregation Differential privacy Data utility Protecció de dades Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| title | Differentially private data publishing via cross-moment microaggregation |
| title_full | Differentially private data publishing via cross-moment microaggregation |
| title_fullStr | Differentially private data publishing via cross-moment microaggregation |
| title_full_unstemmed | Differentially private data publishing via cross-moment microaggregation |
| title_short | Differentially private data publishing via cross-moment microaggregation |
| title_sort | Differentially private data publishing via cross-moment microaggregation |
| topic | Data protection Data privacy Microaggregation Differential privacy Data utility Protecció de dades Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| topic_facet | Data protection Data privacy Microaggregation Differential privacy Data utility Protecció de dades Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| url | https://hdl.handle.net/2117/386400 https://dx.doi.org/10.1016/j.inffus.2019.06.011 |