Litoiu, MarinGoyal, Payal2021-11-152021-11-152021-052021-11-15http://hdl.handle.net/10315/38662With the surging demand for Internet of Things (IoT) healthcare applications, a myriad of data privacy concerns come to light. Cloud computing inherits the risks of exposing data to re-identification vulnerabilities. A secure solution is storing and processing data locally on edge, but it lacks the provision of powerful machine learning (ML) needs. An improved computing framework is required to incorporate ML capabilities and user-data confidentiality. We perform a systematic study of IoT healthcare systems and propose a three-tier architecture that protects and enables data sharing. The edge anonymizes data using differential privacy (DP); transmits it to the cloud to train ML classifier; sent back trained classifier to edge to make inferences. Our findings show 1) XgBoost classifier performs relatively well; classifiers' accuracy trained using DP data is close to that of original data 2) Round-trip execution performance of architecture shows high average mean and variance with higher privacy budgets.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Information technologyElectronic Thesis or Dissertation2021-11-15Data privacyDifferential privacyData anonymizationMachine learningPrivacy-preservingEdgeCloudHealthcareIoTClassificationNon-perturbative maskingStatistical disclosure controlData transformationArchitectureData sharingPrivacy modelThree-tierSystematic study