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

by   Xuebin Ren, et al.

In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time collaborative statistical estimation by disseminating statistics from their separate databases. Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowd-sourcing participants. For mitigating the privacy concern, while traditional differential privacy (DP) mechanism can be simply implemented to perturb the statistics in each timestamp and independently for each dimension, this may suffer a great utility loss from the real-time and multi-dimensional crowd-sourced data. Also, the real-time broadcasting would bring significant overheads in the whole network. To tackle the issues, we propose a novel privacy-preserving and communication-efficient decentralized statistical estimation algorithm (DPCrowd), which only requires intermittently sharing the DP protected parameters with one-hop neighbors by exploiting the temporal correlations in real-time crowd-sourced data. Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multi-dimensional infinite crowd-data streams. Extensive experiments on several datasets demonstrate that our proposed schemes DPCrowd and DPCrowd+ can significantly outperform existing schemes in providing accurate and consensus estimation with rigorous privacy protection and great communication efficiency.



page 4

page 5

page 8

page 9

page 10

page 11

page 13

page 16


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

Among existing privacy-preserving approaches, Differential Privacy (DP) ...

Towards Differentially Private Truth Discovery for Crowd Sensing Systems

Nowadays, crowd sensing becomes increasingly more popular due to the ubi...

Near-Optimal Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding

Motivation: Researchers need a rich trove of genomic datasets that they ...

Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations

Differential Privacy (DP) has received increasing attention as a rigorou...

GenShare: Sharing Accurate Differentially-Private Statistics for Genomic Datasets with Dependent Tuples

Motivation: Cutting the cost of DNA sequencing technology led to a quant...

Multi-Party Computation in IoT for Privacy-Preservation

Preservation of privacy has been a serious concern with the increasing u...

Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance

Crowd simulation is a central topic in several fields including graphics...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.