{"title":"生物医学数据保护性共享的经济模型设计","authors":"Adebayo Ot","doi":"10.32474/ctcsa.2019.01.000117","DOIUrl":null,"url":null,"abstract":"Frequency (MAF) and regression coefficients can lead to privacy concerns [5]. This understanding has led various groups removing statistical data from public databases into access-controlled format. Though such protections help preserve privacy, they also have adverse effects on access to useful dataset for medical research. The medical community is at a crossroad; how can researchers access medical data to data to save lives and still ensure the privacy of the individuals involved in the datasets. An effective model for genomic data dissemination can be achieved through an approach based on game theory to account for adversarial behaviors and capabilities. The proposed approach has already been used to analyze the reidentification risk and proven effective in some risk inherent domains, such as airport security and coast guard patrols [6]. Methodologies are borrowed from game theory to develop an effective, measurable protections for genomic data sharing. This method accounts for adversarial behavior to balance risks against utility more effectively compared with traditional approaches. Abstract Sharing medical data (such as genomic) can lead to important discoveries in healthcare, but researches have shown that links between de-identified data and named persons are sometimes reestablished by users with malicious intents. Traditional approaches to curb this menace rely data use agreements, suppression and noise adding to protect the privacy of individual in the dataset, but this reduces utility of the data. Therefore, this paper proposed an economic game theoretic model design for quantifiable protections of genomic data. The model can be developed to find solution for sharing summary statistics under an economically motivated recipient’s (adversary) inference attack. The framework incorporates four main participants: Data Owners, Certified Institution (CI), Sharer and Researchers (Recipients). The data Sharer and Researcher (who are the players) are economically motivated.","PeriodicalId":303860,"journal":{"name":"Current Trends in Computer Sciences & Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of an Economic Model for Protectively Sharing Biomedical Data\",\"authors\":\"Adebayo Ot\",\"doi\":\"10.32474/ctcsa.2019.01.000117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Frequency (MAF) and regression coefficients can lead to privacy concerns [5]. This understanding has led various groups removing statistical data from public databases into access-controlled format. Though such protections help preserve privacy, they also have adverse effects on access to useful dataset for medical research. The medical community is at a crossroad; how can researchers access medical data to data to save lives and still ensure the privacy of the individuals involved in the datasets. An effective model for genomic data dissemination can be achieved through an approach based on game theory to account for adversarial behaviors and capabilities. The proposed approach has already been used to analyze the reidentification risk and proven effective in some risk inherent domains, such as airport security and coast guard patrols [6]. Methodologies are borrowed from game theory to develop an effective, measurable protections for genomic data sharing. This method accounts for adversarial behavior to balance risks against utility more effectively compared with traditional approaches. Abstract Sharing medical data (such as genomic) can lead to important discoveries in healthcare, but researches have shown that links between de-identified data and named persons are sometimes reestablished by users with malicious intents. Traditional approaches to curb this menace rely data use agreements, suppression and noise adding to protect the privacy of individual in the dataset, but this reduces utility of the data. Therefore, this paper proposed an economic game theoretic model design for quantifiable protections of genomic data. The model can be developed to find solution for sharing summary statistics under an economically motivated recipient’s (adversary) inference attack. The framework incorporates four main participants: Data Owners, Certified Institution (CI), Sharer and Researchers (Recipients). The data Sharer and Researcher (who are the players) are economically motivated.\",\"PeriodicalId\":303860,\"journal\":{\"name\":\"Current Trends in Computer Sciences & Applications\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Trends in Computer Sciences & Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32474/ctcsa.2019.01.000117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Trends in Computer Sciences & Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32474/ctcsa.2019.01.000117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of an Economic Model for Protectively Sharing Biomedical Data
Frequency (MAF) and regression coefficients can lead to privacy concerns [5]. This understanding has led various groups removing statistical data from public databases into access-controlled format. Though such protections help preserve privacy, they also have adverse effects on access to useful dataset for medical research. The medical community is at a crossroad; how can researchers access medical data to data to save lives and still ensure the privacy of the individuals involved in the datasets. An effective model for genomic data dissemination can be achieved through an approach based on game theory to account for adversarial behaviors and capabilities. The proposed approach has already been used to analyze the reidentification risk and proven effective in some risk inherent domains, such as airport security and coast guard patrols [6]. Methodologies are borrowed from game theory to develop an effective, measurable protections for genomic data sharing. This method accounts for adversarial behavior to balance risks against utility more effectively compared with traditional approaches. Abstract Sharing medical data (such as genomic) can lead to important discoveries in healthcare, but researches have shown that links between de-identified data and named persons are sometimes reestablished by users with malicious intents. Traditional approaches to curb this menace rely data use agreements, suppression and noise adding to protect the privacy of individual in the dataset, but this reduces utility of the data. Therefore, this paper proposed an economic game theoretic model design for quantifiable protections of genomic data. The model can be developed to find solution for sharing summary statistics under an economically motivated recipient’s (adversary) inference attack. The framework incorporates four main participants: Data Owners, Certified Institution (CI), Sharer and Researchers (Recipients). The data Sharer and Researcher (who are the players) are economically motivated.