TY - JOUR
T1 - Privacy-Aware Data Trading
AU - Wang, Shengling
AU - Shi, Lina
AU - Hu, Qin
AU - Zhang, Junshan
AU - Cheng, Xiuzhen
AU - Yu, Jiguo
N1 - Funding Information:
Manuscript received September 14, 2020; revised March 18, 2021 and May 11, 2021; accepted July 5, 2021. Date of publication July 26, 2021; date of current version August 17, 2021. This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2102600, in part by the National Natural Science Foundation of China under Grant 61772080, Grant 62972044, and Grant 61832012, in part by the Blockchain Core Technology Strategic Research Program of the Ministry of Education of China under Grant 2020KJ010301, in part by the International Joint Research Project of Faculty of Education, Beijing Normal University, and in part by the Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Lejla Batina. (Corresponding author: Jiguo Yu.) Shengling Wang and Lina Shi are with the School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China (e-mail: wangshengling@bnu.edu.cn; 201821210031@mail.bnu.edu.cn).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.
AB - The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.
KW - Data trading
KW - privacy leakage
KW - the noisy sequential game
KW - the zero-determinant strategies
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U2 - 10.1109/TIFS.2021.3099699
DO - 10.1109/TIFS.2021.3099699
M3 - Article
AN - SCOPUS:85112645386
VL - 16
SP - 3916
EP - 3927
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
SN - 1556-6013
M1 - 9494476
ER -