Privacy-preserving Crowdsourcing Research Articles

PDMSC: privacy-preserving decentralized multi-skill spatial crowdsourcing

Purpose The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. Design/methodology/approach This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. Findings This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. Originality/value This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.

Large-Scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing

Large-Scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing

Solution Probing Attack Against Coin Mixing Based Privacy-Preserving Crowdsourcing Platforms

Solution Probing Attack Against Coin Mixing Based Privacy-Preserving Crowdsourcing Platforms

PrivRo: A Privacy-Preserving Crowdsourcing Service with Robust Quality Awareness

PrivRo: A Privacy-Preserving Crowdsourcing Service with Robust Quality Awareness

FedCrowd: A Federated and Privacy-Preserving Crowdsourcing Platform on Blockchain

Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this article, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.

A Fog-Assisted Privacy-Preserving Task Allocation in Crowdsourcing

As a people-centric sensing paradigm, crowdsourcing has attracted considerable attention since it can solve a complicated task by gathering the wisdom of a crowd of workers. To increase the efficiency of task allocating and recruit suitable workers, the crowdsourcing server needs to obtain information related to task content and worker profiles, which poses a threat to privacy leaks of both tasks and workers. Although several privacy-preserving crowdsourcing mechanisms with simultaneously achieving task privacy and worker privacy have been proposed, there are two problems for these schemes, either the ability of workers is not taken into account or they are computationally intensive. To address the above problems simultaneously, fog-assisted privacy-preserving task allocation in crowdsourcing is proposed by means of the advantages of fog computing, which can not only achieve privacy protection of both the task and the worker but also alleviate the workers’ computational burden by offloading partial computation to the fog node. By applying threshold secret sharing technology, the proposed scheme enables that only workers satisfying task requirements can decrypt the task content, which achieves the verification of the workers’ ability and resists the attacks of the greedy workers. Then, rigorous security proofs about privacy properties are given in the proposed scheme. Finally, the proposed scheme is estimated through theoretical analysis and experiments. The experimental results show that compared with the current state-of-the-art scheme, the proposed scheme has more advantages in terms of computational cost and storage overhead. Especially for a worker, it only requires one pairing and two multiplication operations in the decrypting phase.

Anonymous Privacy-Preserving Task Matching in Crowdsourcing

With the development of sharing economy, crowdsourcing as a distributed computing paradigm has become increasingly pervasive. As one of indispensable services for most crowdsourcing applications, task matching has also been extensively explored. However, privacy issues are usually ignored during the task matching and few existing privacy-preserving crowdsourcing mechanisms can simultaneously protect both task privacy and worker privacy. This paper systematically analyzes the privacy leaks and potential threats in the task matching and proposes a single-keyword task matching scheme for the multirequester/multiworker crowdsourcing with efficient worker revocation. The proposed scheme not only protects data confidentiality and identity anonymity against the crowd-server, but also achieves query traceability against dishonest or revoked workers. Detailed privacy analysis and thorough performance evaluation show that the proposed scheme is secure and feasible.

PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response

Randomized response mechanisms for guaranteeing crowdsourcing data privacy have attracted scholarly attention; aggregators can ensure privacy by collecting only randomized data, and individuals can have plausible deniability regarding their responses. With these mechanisms, analysts employed by organizations can still make predictions and conduct analyses using the randomized data. Existing randomized response-based data collection solutions have severely restricted functionality and usability, resulting in impractical and inefficient systems. Therefore, we developed a randomized response-based privacy-preserving crowdsourcing data collection and analysis mechanism. We designed a complementary randomized response (C-RR) method to guarantee individuals’ data privacy and to preserve features from the original data for analysis. We formalized a machine learning framework; our proposed method uses randomized data in the form of binary vectors to generate a learning network. Extensive experiments on real-world data sets demonstrated that our heavy-hitters estimation scheme, which applies C-RR and our data learning model, significantly outperformed existing estimation schemes in terms of data analysis.

Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries

Abstract Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees. A recent such technology, RAPPOR [12], enables estimation of the marginal frequencies of a set of strings via privacy-preserving crowdsourcing. However, this original estimation process relies on a known dictionary of possible strings; in practice, this dictionary can be extremely large and/or unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of “unknown unknowns,” i.e., strings we do not know we should be estimating. To enable learning without explicit dictionary knowledge, we develop methodology for estimating the joint distribution of multiple variables collected with RAPPOR. Our contributions are not RAPPOR-specific, and can be generalized to other local differential privacy mechanisms for learning distributions of string-valued random variables.