Aggregating Crowd Wisdom via Blockchain: A Private, Correct, and Robust Realization

Duan, Huayi; Zheng, Yifeng; Du, Yuefeng; Zhou, Anxin; Au, Man Ho

doi:10.1109/percom.2019.8767412

Cited by 47 publications

(25 citation statements)

References 31 publications

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“…To the best of our knowledge, there is no study on the examination of Bitcoin in terms of differential privacy in the literature. There are studies combining differential privacy and blockchain [20,21,22,23,24] mostly in general areas. Privacy-preserving solutions for general blockchain structure were studied in [20], and differential privacy was mentioned as a potential solution very briefly.…”

Section: A Related Workmentioning

confidence: 99%

“…There are studies combining differential privacy and blockchain [20,21,22,23,24] mostly in general areas. Privacy-preserving solutions for general blockchain structure were studied in [20], and differential privacy was mentioned as a potential solution very briefly. Differential privacy was used in [21] while aggregating crowd data via blockchain by a service provider before sharing it with a data consumer.…”

Section: A Related Workmentioning

confidence: 99%

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Investigation and Application of Differential Privacy in Bitcoin

Khalilov

Lévi

2022

IEEE Access

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Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy. Generally used methods for improvement include mixing protocols, ring signatures, zero-knowledge proofs, homomorphic commitments, and off-chain storage systems. To the best of our knowledge, in the literature, there is no study examining Bitcoin in terms of differential privacy, which is a privacy notion coming up with some mechanisms that enable running useful statistical queries without identifying any personal information. In this paper, we provide a theoretical examination of differential privacy in Bitcoin. Our motivation arises from the idea that the Bitcoin public blockchain structure can benefit from differential privacy mechanisms for improved privacy, both making anonymization and privacy breaches by direct queries impossible, and preserving the checkability of the integrity of the blockchain. We first examine the current Bitcoin implementation for four query functions using the differential privacy formulation. Then, we present the feasibility of the utilization of two differential privacy mechanisms in Bitcoin; the noise addition to the transaction amounts and the user graph perturbation. We show that these mechanisms decrease the fraction of the cases violating differential privacy, therefore they can be used for improving anonymity and privacy in Bitcoin. Moreover, we showcase the noise addition to transaction amounts by using IBM Differential Privacy Library. We compare four differential privacy mechanisms for varying privacy parameter values and determine the feasible mechanisms and the parameters.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Investigation and Application of Differential Privacy in Bitcoin

Khalilov

Lévi

2022

IEEE Access

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“…To improve the blockchain's performance and guarantee its security, previous works [4][5][6][7] have used a TEE as an off-chain component, decoupling from the blockchain to provide efficient computation and eventually persisting the state data in the blockchain. Matetic et al [5] presented an approach that protects the privacy of light clients in Bitcoin by leveraging TEEs.…”

Section: Tee and Blockchainsmentioning

confidence: 99%

“…A trusted execution environment (TEE) [1] has been used in many applications [2][3][4] to provide secure and trusted computing. It is widely used to be a secure component to address the performance issues of blockchain-based systems [4][5][6][7][8], in which a common architecture adopted by the systems is to decouple the TEE from the blockchain to provide off-chain trusted computing. Its design extends trust from the blockchain to the TEE to improve system performance.…”

Section: Introductionmentioning

confidence: 99%

Distributed Trusted Computing for Blockchain-Based Crowdsourcing

Liang¹,

Li²,

Shin³

2021

Computers, Materials &Amp; Continua

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A centralized trusted execution environment (TEE) has been extensively studied to provide secure and trusted computing. However, a TEE might become a throughput bottleneck if it is used to evaluate data quality when collecting large-scale data in a crowdsourcing system. It may also have security problems compromised by attackers. Here, we propose a scheme, named dTEE, for building a platform for providing distributed trusted computing by leveraging TEEs. The platform is used as an infrastructure of trusted computations for blockchain-based crowdsourcing systems, especially to securely evaluate data quality and manage remuneration: these operations are handled by a TEE group. First, dTEE uses a public blockchain with smart contracts to manage TEEs without reliance on any trusted third parties. Second, to update TEE registration information and rule out zombie TEEs, dTEE uses a reporting mechanism. To attract TEE owners to join in and provide service of trusted computations, it uses a fair monetary incentive mechanism. Third, to account for malicious attackers, we design a model with Byzantine fault tolerance, not limited to a crash-failure model. Finally, we conduct an extensive evaluation of our design on a local cluster. The results show that dTEE finishes evaluating 10,000 images within one minute and achieves about 65 tps throughput when evaluating Sudoku solution data with collective signatures both in a group of 120 TEEs.

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“…The transparency and correctness of computation on the blockchain [24] ensure that the whole coupon redemption process is transparent and correctly enforced. This guarantees that a successfully redeemed coupon must be valid (through enforced signature verification) and cannot be reused; the vendor cannot cheat in the coupon redemption rates for underpaying/repudiating services fees to the broker.…”

Section: Security Analysismentioning

confidence: 99%

A Privacy-Preserving System for Targeted Coupon Service

et al. 2019

Self Cite

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It is increasingly popular to use behavioral targeting in online coupon service for targeted coupon delivery, wherein customized coupons can be delivered to only eligible users whose behavioral profiles match targeting profiles specified by the vendor. While allowing the vendor to personalize the service and build loyalty, such practice also raises the security challenges of protecting users' behavioral profiles and the vendor' targeting profiles. Our research caters for the trend of targeted coupons yet aims to offer privacy assurance. Unlike prior works which are largely constrained by the mere support for secure behavioral targeting, we ambitiously target a new system design that uniquely provides comprehensive functionalities to satisfy practical needs. Specifically, we explore and present the first full-fledged system design for targeted coupon service with three practical functionalities: (i) Private range search on coupon discounts: it enables a user to securely search for encrypted targeted coupons with certain discount range; (ii) Private behavioral targeting: it ensures that only eligible users can decrypt the ciphertexts of targeted coupons obtained from search; (iii) Private blockchain-empowered coupon redemption: it enables coupon redemption to be securely and transparently settled on the blockchain. We conduct extensive experiments and the results show that our secure system design is practically affordable.INDEX TERMS Privacy, targeted coupon service, secure range search, behavioral targeting, blockchains.

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Aggregating Crowd Wisdom via Blockchain: A Private, Correct, and Robust Realization

Cited by 47 publications

References 31 publications

Investigation and Application of Differential Privacy in Bitcoin

Investigation and Application of Differential Privacy in Bitcoin

Distributed Trusted Computing for Blockchain-Based Crowdsourcing

A Privacy-Preserving System for Targeted Coupon Service

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