Dynamic Private Task Assignment under Differential Privacy

02/19/2023
by   Leilei Du, et al.
0

Data collection is indispensable for spatial crowdsourcing services, such as resource allocation, policymaking, and scientific explorations. However, privacy issues make it challenging for users to share their information unless receiving sufficient compensation. Differential Privacy (DP) is a promising mechanism to release helpful information while protecting individuals' privacy. However, most DP mechanisms only consider a fixed compensation for each user's privacy loss. In this paper, we design a task assignment scheme that allows workers to dynamically improve their utility with dynamic distance privacy leakage. Specifically, we propose two solutions to improve the total utility of task assignment results, namely Private Utility Conflict-Elimination (PUCE) approach and Private Game Theory (PGT) approach, respectively. We prove that PUCE achieves higher utility than the state-of-the-art works. We demonstrate the efficiency and effectiveness of our PUCE and PGT approaches on both real and synthetic data sets compared with the recent distance-based approach, Private Distance Conflict-Elimination (PDCE). PUCE is always better than PDCE slightly. PGT is 50 average when worker range is large enough.

READ FULL TEXT
research
11/29/2017

Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations

Differential Privacy (DP) has received increasing attention as a rigorou...
research
06/27/2023

A New Mathematical Optimization-Based Method for the m-invariance Problem

The issue of ensuring privacy for users who share their personal informa...
research
11/01/2017

Re-DPoctor: Real-time health data releasing with w-day differential privacy

Wearable devices enable users to collect health data and share them with...
research
08/08/2020

A Differentially Private Framework in Spatial Crowdsourcing with Historical Data Learning

Spatial crowdsourcing (SC) is an increasing popular category of crowdsou...
research
10/09/2022

Performances of Symmetric Loss for Private Data from Exponential Mechanism

This study explores the robustness of learning by symmetric loss on priv...
research
05/06/2022

Statistical Data Privacy: A Song of Privacy and Utility

To quantify trade-offs between increasing demand for open data sharing a...
research
05/15/2018

How Private Is Your Voting? A Framework for Comparing the Privacy of Voting Mechanisms

Voting privacy has received a lot of attention across several research c...

Please sign up or login with your details

Forgot password? Click here to reset