An Efficient Privacy-Preserving Algorithm based on Randomized Response in IoT-based Smart Grid

04/09/2018
by   Hui Cao, et al.
0

Among existing privacy-preserving approaches, Differential Privacy (DP) is a powerful tool that can provide privacy-preserving noisy query answers over statistical databases and has been widely adopted in many practical fields. In particular, as a privacy machine of DP, Randomized Aggregable Privacy-Preserving Ordinal Response (RAPPOR) enables strong privacy, efficient, and high-utility guarantees for each client string in data crowdsourcing. However, as for Internet of Things(IoT), such as smart gird, data are often processed in batches. Therefore, developing a new random response algorithm that can support batch-processing tend to make it more efficient and suitable for IoT applications than existing random response algorithms. In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guar-antees for consumer's behaviors, and process a batch of data at each time. Firstly, by applying sparse coding in this algorithm, a behavior signature dictionary is created from the aggregated energy consumption data in fog. Then, we add noise into the behavior signature dictionary by classical randomized response techniques and achieve the differential privacy after data re-aggregation. Through the security analysis with the principle of differential privacy and experimental results verification, we find that our Algorithm can preserve consumer's privacy with-out comprising utility.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/05/2021

Rejoinder: Gaussian Differential Privacy

In this rejoinder, we aim to address two broad issues that cover most co...
research
06/30/2022

DP^2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring

Non-intrusive load monitoring (NILM), which usually utilizes machine lea...
research
09/23/2021

A Validated Privacy-Utility Preserving Recommendation System with Local Differential Privacy

This paper proposes a new recommendation system preserving both privacy ...
research
09/29/2020

DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data

In Internet of Things (IoT) driven smart-world systems, real-time crowd-...
research
04/05/2018

Achieving Differential Privacy against Non-Intrusive Load Monitoring in Smart Grid: a Fog Computing approach

Fog computing, a non-trivial extension of cloud computing to the edge of...
research
03/06/2023

Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting

Most tasks in NLP require labeled data. Data labeling is often done on c...
research
07/24/2019

Privacy Parameter Variation Using RAPPOR on a Malware Dataset

Stricter data protection regulations and the poor application of privacy...

Please sign up or login with your details

Forgot password? Click here to reset