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AssigneeKoninklijke Philips N.V.
InventorsEric Thomas Carlson, Mohammad Shahed Sorower, Sreramkumar Sitaraman Viswanathan, Sreekanth Manakkaparambil Sivanandan, Anshul Jain, Sunil Ranjan Khuntia, Ze He
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The present disclosure is directed to methods and apparatus for centralized de-identification of protected data associated with subjects. In various embodiments, de-identified data may be received (1102) that includes de-identified data set(s) associated with subject(s) that is generated from raw data set(s) associated with the subjects. Each of the raw data set(s) may include identifying feature(s) that are usable to identify the respective subject. At least some of the identifying feature(s) may be absent from or obfuscated in the de-identified data. Labels associated with each of the de-identified data sets may be determined (1104). At least some of the de-identified data sets may be applied (1108) as input across a trained machine learning model to generate respective outputs, which may be compared (1110) to the labels to determine a measure of vulnerability of the de-identified data to re-identification.
A method implemented using one or more processors, comprising:
At least one non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:
A system comprising one or more processors and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:
Dependencies are inferred from the claim text.