Dynamic Private Task Assignment under Differential Privacy
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 promi...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
19.02.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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% to 63% faster than PDCE and can improve 16% utility on
average when worker range is large enough. |
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DOI: | 10.48550/arxiv.2302.09511 |