Incentive Mechanism for Uncertain Tasks under Differential Privacy

Research output: Working paper › Preprint › Research

Standard

Incentive Mechanism for Uncertain Tasks under Differential Privacy. / Jiang, Xikun; Ying, Chenhao; Li, Lei; Wu, Haiqin; Luo, Yuan; Düdder, Boris.

arxiv.org, 2023.

Research output: Working paper › Preprint › Research

Harvard

Jiang, X, Ying, C, Li, L, Wu, H, Luo, Y & Düdder, B 2023 'Incentive Mechanism for Uncertain Tasks under Differential Privacy' arxiv.org.

APA

Jiang, X., Ying, C., Li, L., Wu, H., Luo, Y., & Düdder, B. (2023). Incentive Mechanism for Uncertain Tasks under Differential Privacy. arxiv.org.

Vancouver

Jiang X, Ying C, Li L, Wu H, Luo Y, Düdder B. Incentive Mechanism for Uncertain Tasks under Differential Privacy. arxiv.org. 2023 May 26.

Author

Jiang, Xikun ; Ying, Chenhao ; Li, Lei ; Wu, Haiqin ; Luo, Yuan ; Düdder, Boris. / Incentive Mechanism for Uncertain Tasks under Differential Privacy. arxiv.org, 2023.

Bibtex

@techreport{ebd00e612cff4bdea8035d37f92cdb34,
title = "Incentive Mechanism for Uncertain Tasks under Differential Privacy",
abstract = "Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents HERALD*, an incentive mechanism that addresses these issues through the use of uncertainty and hidden bids. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.",
keywords = "cs.GT, cs.CR, Privacy, Task matching, Crowsourcing, Security",
author = "Xikun Jiang and Chenhao Ying and Lei Li and Haiqin Wu and Yuan Luo and Boris D{\"u}dder",
year = "2023",
month = may,
day = "26",
language = "English",
publisher = "arxiv.org",
type = "WorkingPaper",
institution = "arxiv.org",

}

RIS

TY - UNPB

T1 - Incentive Mechanism for Uncertain Tasks under Differential Privacy

AU - Jiang, Xikun

AU - Ying, Chenhao

AU - Li, Lei

AU - Wu, Haiqin

AU - Luo, Yuan

AU - Düdder, Boris

PY - 2023/5/26

Y1 - 2023/5/26

N2 - Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents HERALD*, an incentive mechanism that addresses these issues through the use of uncertainty and hidden bids. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.

AB - Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents HERALD*, an incentive mechanism that addresses these issues through the use of uncertainty and hidden bids. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.

KW - cs.GT

KW - cs.CR

KW - Privacy

KW - Task matching

KW - Crowsourcing

KW - Security

M3 - Preprint

BT - Incentive Mechanism for Uncertain Tasks under Differential Privacy

PB - arxiv.org

ER -

ID: 355229948