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Game Theory Based Correlated Privacy Preserving Analysis in Big Data

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Game Theory Based Correlated Privacy Preserving Analysis in Big Data. / Wu, Xiaotong; Wu, Taotao; Khan, Maqbool et al.

In: IEEE Transactions on Big Data, 05.05.2017.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Wu X, Wu T, Khan M, Ni Q, Dou W. Game Theory Based Correlated Privacy Preserving Analysis in Big Data. IEEE Transactions on Big Data. 2017 May 5. Epub 2017 May 5. doi: 10.1109/TBDATA.2017.2701817

Author

Wu, Xiaotong ; Wu, Taotao ; Khan, Maqbool et al. / Game Theory Based Correlated Privacy Preserving Analysis in Big Data. In: IEEE Transactions on Big Data. 2017.

Bibtex

@article{d1485400995f4b5a81d2722ca8f83250,
title = "Game Theory Based Correlated Privacy Preserving Analysis in Big Data",
abstract = "Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors{\textquoteright} privacy parameters. In this paper, we firstly present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments.",
keywords = "Differential privacy, privacy preservation, game theory, big data",
author = "Xiaotong Wu and Taotao Wu and Maqbool Khan and Qiang Ni and Wanchun Dou",
note = "{\textcopyright}2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2017",
month = may,
day = "5",
doi = "10.1109/TBDATA.2017.2701817",
language = "English",
journal = "IEEE Transactions on Big Data",
issn = "2332-7790",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Game Theory Based Correlated Privacy Preserving Analysis in Big Data

AU - Wu, Xiaotong

AU - Wu, Taotao

AU - Khan, Maqbool

AU - Ni, Qiang

AU - Dou, Wanchun

N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2017/5/5

Y1 - 2017/5/5

N2 - Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors’ privacy parameters. In this paper, we firstly present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments.

AB - Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors’ privacy parameters. In this paper, we firstly present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments.

KW - Differential privacy

KW - privacy preservation

KW - game theory

KW - big data

U2 - 10.1109/TBDATA.2017.2701817

DO - 10.1109/TBDATA.2017.2701817

M3 - Journal article

JO - IEEE Transactions on Big Data

JF - IEEE Transactions on Big Data

SN - 2332-7790

ER -