Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data

Abstract

Various Internet of Things (IoT) devices generate complex, dynamically changed, and infinite data streams. Adversaries can cause harm if they can access the user's sensitive raw streaming data. For this reason, protecting the privacy of the data streams is crucial. In this paper, we explore local differential privacy techniques for streaming data. We compare the techniques and report the advantages and limitations. We also present the effect on component (e.g., smoother, perturber) variations of distribution-based local differential privacy. We find that combining distribution-based noise during perturbation provides more flexibility to the interested entity.This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan).

Local differential privacy protection for wearable device data

“…Ning Guo (2019) [30] collected data from users' browsing on the scenario of web page click ranking with a mixture of centered differential privacy and local differential privacy. Afrose et al (2021) [31] proposed local differential privacy for stream data using RAPPOR technique to obtain frequency estimation. Arcolezi et al (2021) [32] conducted a study on temporal data of multidimensional attribute using three state-of-the-art protocols.…”