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10.2196/23139 http://scihub22266oqcxt.onion/10.2196/23139 33196453!7704280!33196453     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33196453&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       J+Med+Internet+Res 2020 ; 22 (11): e23139 Nephropedia Template TP {{tp|p=33196453|t=2020. Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation |pdf=|usr=}}{{33196453}} gab.com Text Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation JO: J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=33196453 #FOAvip BACKGROUND: There has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them. OBJECTIVE: The purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data. METHODS: A full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this "meaningful identity disclosure risk." The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data. RESULTS: The meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively. CONCLUSIONS: We have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data. Twit Text FOAVip Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation ????J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=33196453 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33196453 #FOAvip English Wikipedia <ref>{{Cite pmid|33196453}}</ref> Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation #MMPMID33196453 El Emam K; Mosquera L; Bass J J Med Internet Res 2020[Nov]; 22 (11): e23139 PMID33196453show ga BACKGROUND: There has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them. OBJECTIVE: The purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data. METHODS: A full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this "meaningful identity disclosure risk." The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data. RESULTS: The meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively. CONCLUSIONS: We have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data. |COVID-19/*epidemiology[MESH] |Disclosure/*standards[MESH] |Humans[MESH] |Information Dissemination/*methods[MESH] |Reproducibility of Results[MESH] |Risk Factors[MESH]Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Mo(epmc).pdf DeepDyve Pubget Overpricing