An anonymous data reporting strategy with ensuring incentives for mobile crowd-sensing

Li, Yang; Zhao, Yunlong; Ishak, Serrat; Song, Hongtao; Wang, Nianbin; Yao, Nianmin

doi:10.1007/s12652-017-0529-x

Cited by 16 publications

(15 citation statements)

References 16 publications

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“…Li et al leveraged encoded vector to achieve bid privacy preservation in incentive solution in [45]. Owing to bulk transfer technique and public key cryptosystem, it successfully achieves anonymity and unlinkability.…”

Section: A) Privacy Protection In Credit-based Incentive (Cr)mentioning

confidence: 99%

Privacy protection in mobile crowd sensing: a survey

et al. 2019

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The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

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Section: A) Privacy Protection In Credit-based Incentive (Cr)mentioning

confidence: 99%

Privacy protection in mobile crowd sensing: a survey

et al. 2019

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“…However, these third-party components can themselves be points of privacy vulnerability. For example, one protocol [11] relies on the use of an additional component that manages the accounts of both participants and the incentive provider. This credit management component has particular potential for inference attacks as it performs credit transfers from the incentive provider’s account to that of the participant.…”

Section: Related Workmentioning

confidence: 99%

“…There are several approaches in the state of the art that use tokens, which are mappable to a tangible monetary or non-monetary item of value, to allocate rewards. However, this is sometimes at the cost of privacy preservation through the use of third parties ([18] and [11]) and pseudonyms [18]. One credit token system ([28], also [29] and [30]) does attempt to address the challenges of anonymous reward allocation by using a Blind Signature to break the link between the credit token and what is termed the pseudo-credit to ensure that the service provider cannot make any connection with the data submission for which the credit is earned.…”

Section: Related Workmentioning

confidence: 99%

Privacy Aware Incentivization for Participatory Sensing

Connolly

Dusparić

Iosifidis

et al. 2019

Sensors

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Participatory sensing is a process whereby mobile device users (or participants) collect environmental data on behalf of a service provider who can then build a service based upon these data. To attract submissions of such data, the service provider will often need to incentivize potential participants by offering a reward. However, for the privacy conscious, the attractiveness of such rewards may be offset by the fact that the receipt of a reward requires users to either divulge their real identity or provide a traceable pseudonym. An incentivization mechanism must therefore facilitate data submission and rewarding in a way that does not violate participant privacy. This paper presents Privacy-Aware Incentivization (PAI), a decentralized peer-to-peer exchange platform that enables the following: (i) Anonymous, unlinkable and protected data submission; (ii) Adaptive, tunable and incentive-compatible reward computation; (iii) Anonymous and untraceable reward allocation and spending. PAI makes rewards allocated to a participant untraceable and unlinkable and incorporates an adaptive and tunable incentivization mechanism which ensures that real-time rewards reflect current environmental conditions and the importance of the data being sought. The allocation of rewards to data submissions only if they are truthful (i.e., incentive compatibility) is also facilitated in a privacy-preserving manner. The approach is evaluated using proofs and experiments.

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“…If the participants collude, they maybe exchange or duplicate others' pseudonyms for earning more payments. Our previous work [8] proposed a privacy protection scheme with incentive mechanism for mobile crowd-sensing. It uses slot reservation based on shuffle to establish a submission schedule by anonymously assigning a slot in a vector to each member in a group of participants.…”

Section: Introductionmentioning

confidence: 99%

“…In order to address the above issues, this paper proposes a strategy to protect participant's privacy through data reporting anonymity while providing incentives in the form of micropayments. Our contributions are as follows: (1) we present a data reporting protocol for guaranteeing anonymity based on multiplayer DC-nets and bulk transfer with an incentive strategy followed by works [9][10][11][12][13], and adapted to participatory sensing in Yao et al [14] and our previous work [8]. (2) We optimize slot reservation and data submission stage for efficiency, which uses random slot selection instead of previous multiple encryption shuffles.…”

Section: Introductionmentioning

confidence: 99%

Anonymous Data Reporting Strategy with Dynamic Incentive Mechanism for Participatory Sensing

Song

Zhao

et al. 2021

Security and Communication Networks

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Participatory sensing is often used in environmental or personal data monitoring, wherein a number of participants collect data using their mobile intelligent devices for earning the incentives. However, a lot of additional information is submitted along with the data, such as the participant’s location, IP and incentives. This multimodal information implicitly links to the participant’s identity and exposes the participant’s privacy. In order to solve the issue of these multimodal information associating with participants’ identities, this paper proposes a protocol to ensure anonymous data reporting while providing a dynamic incentive mechanism simultaneously. The proposed protocol first establishes a submission schedule by anonymously selecting a slot in a vector by each member where every member and system entities are oblivious of other members’ slots and then uses this schedule to submit the all members’ data in an encoded vector through bulk transfer and multiplayer dining cryptographers networks (DC-nets) . Hence, the link between the data and the member’s identity is broken. The incentive mechanism uses blind signature to anonymously mark the price and complete the micropayments transfer. Finally, the theoretical analysis of the protocol proves the anonymity, integrity, and efficiency of this protocol. We implemented and tested the protocol on Android phones. The experiment results show that the protocol is efficient for low latency tolerable applications, which is the cases with most participatory sensing applications, and they also show the advantage of our optimization over similar anonymous data reporting protocols.

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An anonymous data reporting strategy with ensuring incentives for mobile crowd-sensing

Cited by 16 publications

References 16 publications

Privacy protection in mobile crowd sensing: a survey

Privacy protection in mobile crowd sensing: a survey

Privacy Aware Incentivization for Participatory Sensing

Anonymous Data Reporting Strategy with Dynamic Incentive Mechanism for Participatory Sensing

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