Biblio

With the development of big data and machine learning techniques, medical data sharing for the use of disease diagnosis has received considerable attention. Blockchain, as an emerging technology, has been widely used to resolve the efficiency and security issues in medical data sharing. However, the existing studies on blockchain-based medical data sharing have rarely concerned about the reasonable incentive mechanism. In this paper, we propose a cooperation model where medical data is shared via blockchain. We derive the topological relationships among the participants consisting of data owners, miners and third parties, and gradually develop the computational process of Shapley value revenue distribution. Specifically, we explore the revenue distribution under different consensuses of blockchain. Finally, we demonstrate the incentive effect and rationality of the proposed solution by analyzing the revenue distribution.

Mobile Ad-hoc Networks are dynamic in nature and do not have fixed infrastructure to govern nodes in the networks. The mission lies ahead in coordinating among such dynamically shifting nodes. The root problem of identifying and isolating misbehaving nodes that refuse to forward packets in multi-hop ad hoc networks is solved by the development of a comprehensive system called Audit-based Misbehavior Detection (AMD) that can efficiently isolates selective and continuous packet droppers. AMD evaluates node behavior on a per-packet basis, without using energy-expensive overhearing techniques or intensive acknowledgment schemes. Moreover, AMD can detect selective dropping attacks even in end-to-end encrypted traffic and can be applied to multi-channel networks. Game theoretical approaches are more suitable in deciding upon the reward mechanisms for which the mobile nodes operate upon. Rewards or penalties have to be decided by ensuring a clean and healthy MANET environment. A non-routine yet surprise alterations are well required in place in deciding suitable and safe reward strategies. This work focuses on integrating a Audit-based Misbehaviour Detection (AMD)scheme and an incentive based reputation scheme with game theoretical approach called Supervisory Game to analyze the selfish behavior of nodes in the MANETs environment. The proposed work GAMD significantly reduces the cost of detecting misbehavior nodes in the network.

The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoffs scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibrium that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal.