RASE: Efficient Privacy-Preserving Data Aggregation Against Disclosure Attacks for IoTs

Summary

This research article proposes RASE, a privacy-preserving data aggregation paradigm for IoT devices, as an alternative to earlier schemes that relied on a trusted fusion center or on local perturbation by contributors. RASE runs three steps: noise addition through a differentially private randomizer, random permutation by a shuffler that breaks the link between senders and receivers, and parameter estimation from the noisy data. The authors report extensive simulations exploring the privacy-utility trade-off of RASE.