A Supermodularity-Based Differential Privacy Preserving Algorithm for Data Anonymization
Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suff...
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| Vydáno v: | IEEE transactions on knowledge and data engineering Ročník 26; číslo 7; s. 1591 - 1601 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.07.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1041-4347, 1558-2191 |
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| Abstract | Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or both of the following major problems: scalability and privacy guarantee. Differential Privacy provides a theoretical formulation for privacy that ensures that the system essentially behaves the same way regardless of whether any individual is included in the database. In this paper, we address both scalability and privacy risk of data anonymization. We propose a scalable algorithm that meets differential privacy when applying a specific random sampling. The contribution of the paper is two-fold: 1) we propose a personalized anonymization technique based on an aggregate formulation and prove that it can be implemented in polynomial time; and 2) we show that combining the proposed aggregate formulation with specific sampling gives an anonymization algorithm that satisfies differential privacy. Our results rely heavily on exploring the supermodularity properties of the risk function, which allow us to employ techniques from convex optimization. Through experimental studies we compare our proposed algorithm with other anonymization schemes in terms of both time and privacy risk. |
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| AbstractList | Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or both of the following major problems: scalability and privacy guarantee. Differential Privacy provides a theoretical formulation for privacy that ensures that the system essentially behaves the same way regardless of whether any individual is included in the database. In this paper, we address both scalability and privacy risk of data anonymization. We propose a scalable algorithm that meets differential privacy when applying a specific random sampling. The contribution of the paper is two-fold: 1) we propose a personalized anonymization technique based on an aggregate formulation and prove that it can be implemented in polynomial time; and 2) we show that combining the proposed aggregate formulation with specific sampling gives an anonymization algorithm that satisfies differential privacy. Our results rely heavily on exploring the supermodularity properties of the risk function, which allow us to employ techniques from convex optimization. Through experimental studies we compare our proposed algorithm with other anonymization schemes in terms of both time and privacy risk. Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or both of the following major problems: scalability and privacy guarantee. Differential Privacy provides a theoretical formulation for privacy that ensures that the system essentially behaves the same way regardless of whether any individual is included in the database. In this paper, we address both scalability and privacy risk of data anonymization. We propose a scalable algorithm that meets differential privacy when applying a specific random sampling. The contribution of the paper is two-fold: 1) we propose a personalized anonymization technique based on an aggregate formulation and prove that it can be implemented in polynomial time; and 2) we show that combining the proposed aggregate formulation with specific sampling gives an anonymization algorithm that satisfies differential privacy. Our results rely heavily on exploring the supermodularity properties of the risk function, which allow us to employ techniques from convex optimization. Through experimental studies we compare our proposed algorithm with other anonymization schemes in terms of both time and privacy risk. [PUBLICATION ABSTRACT] |
| Author | Elbassioni, Khaled Bertino, Elisa Fouad, Mohamed R. |
| Author_xml | – sequence: 1 givenname: Mohamed R. surname: Fouad fullname: Fouad, Mohamed R. email: mraouffouad@gmail.com organization: Purdue Univ., West Lafayette, IN, USA – sequence: 2 givenname: Khaled surname: Elbassioni fullname: Elbassioni, Khaled email: elbassio@mpi-inf.mpg.de organization: Max-Planck-Inst. fur Inf., Saarbrücken, Germany – sequence: 3 givenname: Elisa surname: Bertino fullname: Bertino, Elisa email: bertino@purdue.edu organization: Purdue Univ., West Lafayette, IN, USA |
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| References | ref13 ref12 ref14 ref31 ref30 ghinita (ref15) 2007 ref32 ref10 ref2 ref17 ref19 ref18 grätzer (ref16) 2003 dwork (ref9) 2006 cao (ref5) 2011; 8 ref24 aggarwal (ref1) 2005 ref23 ref26 li (ref22) 2011 ref21 lovász (ref25) 2006 dwork (ref7) 2006 ref28 ref27 ref29 ref8 ref4 ref3 ref6 fouad (ref11) 2012 krause (ref20) 0 |
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| Title | A Supermodularity-Based Differential Privacy Preserving Algorithm for Data Anonymization |
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