• Technical Name
  • CollectionAnalysis of Crowdsourced Data Based on Spintronic Randomized Response for Compliance GDPR
  • Operator
  • National Taiwan University
  • Booth
  • Online display only
  • Contact
  • 胡淳惠
  • Email
  • anniehu521@phys.ntu.edu.tw
Technical Description We designed chips of the truly random number generator-based private aggregatable randomized response for crowdsourced data collection following TSMC 18nm process. It makes the data not be subject to the regulations of GDPR. Our system has been validated to ensure data de-identificationhigh utility based on spintronicsany truly random number generator co-design multilayer randomized respo
Scientific Breakthrough Our approach (called SPARR for short) can employ a set of MTJs as a spintronics-based TRNG to derive true random numbers. With the TRNGdesign of four coin flips, SPARR can preserve privacycrowdsource population statistics on data collected from individualsaccurately rebuild the data. Notably, it has been successfully fabricated using TSMC 18nm process.
Industrial Applicability Our de-identification chips have a forward-looking, innovative IoT architectureapplications For private IoT streaming data collection, our chips are currently the only one on the market using TRNG to guarantee data privacy This research is mainly supported by Etron Technology, Inc., which can help Etron Technology, Inc. to prioritize the market of the IoT data privacy applications. And, we
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