Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework

Kargupta, Hillol; Das, Kunal; Liu, Kun

doi:10.1007/978-3-540-74976-9_54

Cited by 52 publications

(45 citation statements)

References 7 publications

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“…They could collude with each other to assign a very low sensitivity level for the photo and specify policies to grant a wider audience to access the photo. We will also investigate a game theoretic mechanism to tackle collusion activities in multiparty privacy control in OSNs with the consideration of the proposed approaches in the recent work [23,30,38].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Game theoretic analysis of multiparty access control in online social networks

Ahn

Zhao

et al. 2014

Proceedings of the 19th ACM Symposium on Access Control Models and Technologies

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Existing online social networks (OSNs) only allow a single user to restrict access to her/his data but cannot provide any mechanism to enforce privacy concerns over data associated with multiple users. This situation leaves privacy conflicts largely unresolved and leads to the potential disclosure of users' sensitive information. To address such an issue, a MultiParty Access Control (MPAC) model was recently proposed, including a systematic approach to identify and resolve privacy conflicts for collaborative data sharing in OSNs. In this paper, we take another step to further study the problem of analyzing the strategic behavior of rational controllers in multiparty access control, where each controller aims to maximize her/his own benefit by adjusting her/his privacy setting in collaborative data sharing in OSNs. We first formulate this problem as a multiparty control game and show the existence of unique Nash Equilibrium (NE) which is critical because at an NE, no controller has any incentive to change her/his privacy setting. We then present algorithms to compute the NE and prove that the system can converge to the NE in only a few iterations. A numerical analysis is also provided for different scenarios that illustrate the interplay of controllers in the multiparty control game. In addition, we conduct user studies of the multiparty control game to explore the gap between game theoretic approaches and real human behaviors.

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Section: Discussion and Future Workmentioning

confidence: 99%

Game theoretic analysis of multiparty access control in online social networks

Ahn

Zhao

et al. 2014

Proceedings of the 19th ACM Symposium on Access Control Models and Technologies

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show abstract

“…This work says nothing about privacy, and solely focuses on regression learning. Finally, the work of Kargupta, et al [19], analyzes each step of a multi-party computation process in terms of game theory, with the focus of preventing cheating withing the process, and removing coalitions from gameplay. Each of these deals with the problem of ensuring truthfulness in data mining.…”

Section: Related Workmentioning

confidence: 99%

Incentive Compatible Privacy-Preserving Distributed Classification

Nix

Kantarciouglu

2012

IEEE Trans. Dependable and Secure Comput.

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Abstract-In this paper, we propose game-theoretic mechanisms to encourage truthful data sharing for distributed data mining. One proposed mechanism uses the classic VickreyClarke-Groves (VCG) mechanism, and the other relies on the Shapley value. Neither relies on the ability to verify the data of the parties participating in the distributed data mining protocol. Instead, we incentivize truth telling based solely on the data mining result. This is especially useful for situations where privacy concerns prevent verification of the data. Under reasonable assumptions, we prove that these mechanisms are incentive compatible for distributed data mining. In addition, through extensive experimentation, we show that they are applicable in practice.

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“…In [24], methods for validating the uncertainty and indistinguishability of a set of releasing views over a private table are proposed. In addition, privacy leakage in a multi-party environment has been investigated [25].…”

Section: Related Workmentioning

confidence: 99%

Privacy Disclosure and Preservation in Learning with Multi-Relational Databases

Guo¹,

Viktor²,

Paquet³

2011

Journal of Computing Science and Engineering

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There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database. Category: Smart and intelligent computing

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Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework

Cited by 52 publications

References 7 publications

Game theoretic analysis of multiparty access control in online social networks

Game theoretic analysis of multiparty access control in online social networks

Incentive Compatible Privacy-Preserving Distributed Classification

Privacy Disclosure and Preservation in Learning with Multi-Relational Databases

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