A Survey of Game Theoretical Privacy Preservation for Data Sharing and Publishing
Privacy preservation has been one of the biggest concerns in data sharing and publishing. The wide-spread application of data sharing and publishing contributes to the utilization of data, but brings a severe risk of privacy leakage. Although the correspo
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Changzhou University, Changzhou, China [email protected] 2 School of Computer Science and Technology, Nanjing Normal University, Nanjing, China {wuxiaotong,73025,glji}@njnu.edu.cn 3 Huawei Company, Shenzhen, China [email protected] 4 Department of Computing, Macquarie University, Macquarie Park, Australia [email protected] 5 The State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China [email protected]
Abstract. Privacy preservation has been one of the biggest concerns in data sharing and publishing. The wide-spread application of data sharing and publishing contributes to the utilization of data, but brings a severe risk of privacy leakage. Although the corresponding privacy preservation techniques have been proposed, it is inevitable to decrease the accuracy of data. More importantly, it is a challenge to analyze the behaviors and interactions among different participants, including data owners, collectors and adversaries. For data owners and collectors, they need to select proper privacy preservation mechanisms and parameters to maximize their utility under a certain amount of privacy guarantee. For data adversaries, their objective is to get the sensitive information by various attack measurements. In this paper, we survey the related work of game theorybased privacy preservation under data sharing and publishing. We also discuss the possible trends and challenges of the future research. Our survey provides a systematic and comprehensive understanding about privacy preservation problems under data sharing and publishing. Keywords: Game theory sharing and publishing
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· Data privacy · Nash equilibrium · Data
Introduction
With the fast development of communications and infrastructures, there is a huge volume of data generated by various devices, including smart phones and c Springer Nature Singapore Pte Ltd. 2020 S. Yu et al. (Eds.): SPDE 2020, CCIS 1268, pp. 205–216, 2020. https://doi.org/10.1007/978-981-15-9129-7_15
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wearable devices [24]. Data sharing and publishing greatly improve the convenience of the daily life of peoples by data analysis techniques, such as service recommendation and data mining [5,9,19–23,44]. For example, some mobile user sends his/her health information collected by a smart watch to medical experts and gets a scientific sport plan. Mobile users also take advantage of their own data to get others’ applications and services, such as GPS navigation, shopping and takeaway. The data collectors (e.g., hospitals) share medical data to disease prevention departments to predict possible infectious diseases (e.g., coronavirus disease 2019, COVID-19 [39]). Although users benefit from the applications and services, it brings a certain amount of leakage risk of their sensitive information. Since data sent to service providers is left from data owners (e.g., mobile users), they cannot control the usage of the data. The risk may cause monetary or reputation loss of users to hinder data sharing and publishing [38,40]. In recent years, there have
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