Differentially Private Mobile Crowd Sensing Considering Sensing Errors
Abstract
:1. Introduction
2. Models
2.1. Application Model
2.2. Motivating Example
2.3. Privacy Metric
2.4. Utility Metric
2.5. Problem
3. Related Work
3.1. Privacy-Preserving Mobile Crowdsensing
3.2. Privacy Metrics
3.3. Incentive Mechanism and Trustworthiness for Mobile Crowdsensing
4. Method
4.1. Overview
4.2. PDE for Participants
Algorithm 1 Anonymization Algorithm. |
Input:, , , , , , . Output: Report value v and standard deviation of sensing error
|
4.3. ETE for Estimation
Algorithm 2 Estimation Algorithm. |
Input:Y, , , , , , , , , Output:
|
5. Evaluation
5.1. Evaluation of Synthetic Data
5.2. Evaluation of Real Data
5.2.1. Location Data
5.2.2. Deep Neural Network’s Output Data
6. Discussion
7. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Methods | Output of the Method |
---|---|
Existing methods. | The estimated distribution of sensing data containing errors. |
Our method. | The estimated distribution of true data without errors. |
N | Number of participants. |
True sensing value of participant i. | |
Reported sensing value of participant i. | |
X | . |
Y | . |
Set of standard deviations of the normal distributions of sensing errors of all participants. | |
Number of bins of a histogram. | |
Maximum value of a sensing data. | |
Minimum value of a sensing data. | |
Maximum value of a reported data. | |
Minimum value of a reported data. | |
Maximum value of a standard deviation. | |
Minimum value of a standard deviation. | |
Scale factor of a Laplace noise with regard to the sensing value. | |
Scale factor of a Laplace noise with regard to the standard deviation. | |
Number of participants whose reported values were categorized into the ith bin. | |
Estimated number of participants whose true values were categorized into the ith bin. | |
† | . |
‡ | . |
Layer ID | Description of Each Layer |
---|---|
1 | Input Layer |
2 | Convolutional Layer |
3 | Convolutional Layer |
4 | Max Pooling Layer |
5 | Convolutional Layer |
6 | Convolutional Layer |
7 | Max Pooling Layer |
8 | Convolutional Layer |
9 | Convolutional Layer |
10 | Convolutional Layer |
11 | Max Pooling Layer |
12 | Convolutional Layer |
13 | Convolutional Layer |
14 | Convolutional Layer |
15 | Max Pooling Layer |
16 | Convolutional Layer |
17 | Convolutional Layer |
18 | Convolutional Layer |
19 | Max Pooling Layer |
20 | Convolutional Layer |
21 | Convolutional Layer |
22 | Fully Connected Layer |
23 | Output Layer |
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Sei, Y.; Ohsuga, A. Differentially Private Mobile Crowd Sensing Considering Sensing Errors. Sensors 2020, 20, 2785. https://doi.org/10.3390/s20102785
Sei Y, Ohsuga A. Differentially Private Mobile Crowd Sensing Considering Sensing Errors. Sensors. 2020; 20(10):2785. https://doi.org/10.3390/s20102785
Chicago/Turabian StyleSei, Yuichi, and Akihiko Ohsuga. 2020. "Differentially Private Mobile Crowd Sensing Considering Sensing Errors" Sensors 20, no. 10: 2785. https://doi.org/10.3390/s20102785
APA StyleSei, Y., & Ohsuga, A. (2020). Differentially Private Mobile Crowd Sensing Considering Sensing Errors. Sensors, 20(10), 2785. https://doi.org/10.3390/s20102785