A Privacy Protection Model of Data Publication Based on Game Theory

Kuang, Li; Zhu, Yujia; Li, Shuqi; Yan, Xuejin; Yan, Han

doi:10.1155/2018/3486529

Cited by 18 publications

(15 citation statements)

References 22 publications

(23 reference statements)

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“…We will continue to further the analysis of influencing factors and consider the impacts of different ways of collaborating to optimize our forecasting methods. The thing we want to know is the relationship between the ecosystem health of open source software and the privacy protection of contributors [22]. However, From Figure 11, project elasticsearch shows the best health result in eight projects in June 2015, and the next is react, followed by django, bootstrap.…”

Section: Discussionmentioning

confidence: 99%

Healthy or Not: A Way to Predict Ecosystem Health in GitHub

Liao

Yan

et al. 2019

Symmetry

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With the development of open source community, through the interaction of developers, the collaborative development of software, and the sharing of software tools, the formation of open source software ecosystem has matured. Natural ecosystems provide ecological services on which human beings depend. Maintaining a healthy natural ecosystem is a necessity for the sustainable development of mankind. Similarly, maintaining a healthy ecosystem of open source software is also a prerequisite for the sustainable development of open source communities, such as GitHub. This paper takes GitHub as an example to analyze the health condition of open source ecosystem and, also, it is a research area in Symmetry. Firstly, the paper presents the healthy definition of GitHub open source ecosystem health and, then, according to the main components of natural ecosystem health, the paper proposes the health indicators and health indicators evaluation method. Based on the above, the GitHub ecosystem health prediction method is proposed. By analyzing the projects and data collected in GitHub, it is found that, using the proposed evaluation indicators and method, we can analyze the healthy development trend of the GitHub ecosystem and contribute to the stability of ecosystem development. Symmetry 2019, 11, 144 3 of 16 sustainability in natural ecosystems. Liao et al. [20] measured the sustainability of open source software ecosystem from the aspects of openness, stability, activity, and extensibility, and applied the assessment method of open source software ecosystem sustainability from the target level, criterion level, and measurement level to stack overflow.At present, the research of software ecosystem mainly focuses on system structure, participants, and ecosystem characteristics. The research on ecosystem health is mainly based on its theoretical research, and there is less quantitative analysis of the health indicators. Therefore, it is worthwhile to get into the questions of how to analyze the GitHub ecosystem health quantitatively, and how to predict the healthiness of the ecosystem. GitHub Ecosystem Health Prediction MethodIn this section, the working process of GitHub ecosystem and the various behaviors of users in the GitHub ecosystem will be introduced in Section 3.1, and then followed by the Health Prediction Model of GitHub Ecosystem (HPGE model). Figure 1 is an overview of the HPGE model. Symmetry 2019, 11, x FOR PEER REVIEW 3 of 16 sustainability in natural ecosystems. Liao et al. [20] measured the sustainability of open source software ecosystem from the aspects of openness, stability, activity, and extensibility, and applied the assessment method of open source software ecosystem sustainability from the target level, criterion level, and measurement level to stack overflow.At present, the research of software ecosystem mainly focuses on system structure, participants, and ecosystem characteristics. The research on ecosystem health is mainly based on its theoretical research, and there is less quantitative analysis o...

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Section: Discussionmentioning

confidence: 99%

Healthy or Not: A Way to Predict Ecosystem Health in GitHub

Liao

Yan

et al. 2019

Symmetry

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“…As a typical mathematical method, game theory has been widely used in network security Tong and Zhong (2016), privacy protection Ying and Nayak (2017); Kuang et al (2018); Xiong et al (2018); Zhou et al (2019) and wireless networks Do et al (2017) in recent years. Do et al (2017) discussed the existing game theory-based security protection features, including physical security, communication architecture, privacy protection, and defense mechanisms.…”

Section: Related Workmentioning

confidence: 99%

VMPS: A Vector Map Protection Framework Supporting Dynamic Game and Local Desensitization

Hua

Duan

et al. 2022

Preprint

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Secure sharing of geospatial information has attracted widespread attention in recent years. As a typical geospatial data, vector maps can be used in many fields of national economic and social development. However, there are many security threats to data sharing of vector digital map, such as tampering, forgery, infringement, data leakages. Due to the complex spatial relation nature, existing data protection solutions are either infeasible or ineffective in such scenarios, especially for the exchange, sharing, and publishing of map data. To address these real security problems, in this paper, we propose a novel vector map protection framework, which introduces the idea of dynamic game and local desensitization. Two basic models and a specific scheme, including an important information protection model, an optimal data desensitization model, and a game-based local desensitization scheme, are presented in this framework. Dynamic game strategies can be used to effectively measure the utility of players under different decisions. Furthermore, important or sensitive map elements in vector maps can be desensitized locally in a controlled and reversible manner , thereby achieving more efficient data protection and secure sharing. A comprehensive evaluation, including desensitization strategies, performance and time consumption, accuracy control and reversibility, is given to show the security and feasibility of the proposed framework.

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“…Hu Z and Wu W [28,29] studied the offense-defense mechanism of Topcoder.com contests, constructed a game theoretical model, and calculated the Nash equilibrium to deduce these developers' specific behavior decisions. Kuang L, Zhu Y, Li S, et al [30] used game theory in data privacy protection. Other studies paid attention to the correlation between developers and the reward of tasks.…”

Section: Research On Crowd Workersmentioning

confidence: 99%

A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study

Liao

Zhang

et al. 2019

Applied Sciences

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Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward.

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A Privacy Protection Model of Data Publication Based on Game Theory

Cited by 18 publications

References 22 publications

Healthy or Not: A Way to Predict Ecosystem Health in GitHub

Healthy or Not: A Way to Predict Ecosystem Health in GitHub

VMPS: A Vector Map Protection Framework Supporting Dynamic Game and Local Desensitization

A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study

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