Differentially Private Double Spectrum Auction With Approximate Social Welfare Maximization

Chen, Zhili; Ni, Tianjiao; Zhong, Hong; Zhang, Shun; Cui, Jie

doi:10.1109/tifs.2019.2908070

Cited by 33 publications

(11 citation statements)

References 29 publications

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“…However, there may still be privacy leaks if one infers their sensitive information from the auction's outcomes. Differential privacy techniques can help to combat this issue [109]. The current differentially private auction mechanisms are mostly designed for spectrum allocation in wireless communication.…”

Section: Differential Privacy In Auctionmentioning

confidence: 99%

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More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Zhu¹,

Ye²,

Wang³

et al. 2022

IEEE Trans. Knowl. Data Eng.

105

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Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.

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Section: Differential Privacy In Auctionmentioning

confidence: 99%

“…Chen et al [109] designed a differentially private double spectrum auction mechanism. Their mechanism is a uniform price auction mechanism, where all sellers are paid with a selling clearing price and all buyers groups are charged with a buying clearing price.…”

Section: Differential Privacy In Auctionmentioning

confidence: 99%

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Zhu¹,

Ye²,

Wang³

et al. 2022

IEEE Trans. Knowl. Data Eng.

105

View full text Add to dashboard Cite

show abstract

“…When the resource allocation market of edge computing is established, one of the popular market mechanisms in the industry-auction model will be introduced to solve the problem of resource allocation. Auction model is considered to be a feasible solution to numerous transaction problems, and it has been applied in many systems, including spectrum switching, smart grid configuration and cloud resource allocation [6,10,20,31,[37][38][39]. Zhou et al [39] used double auctions to trade spectrum earlier.…”

Section: Related Workmentioning

confidence: 99%

DPODA: Differential Privacy-based Online Double Auction for Pervasive Edge Computing Resource Allocation

Sun

et al. 2020

Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure

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In recent years, edge computing transforms the network edge into an intelligent platform to provide better services for users by making storage, computing, control and network function closer to end-users, things, and sensors. However, due to the limitation of edge resources and the diversity of edge resource providers, there is no good mechanism to solve the resource allocation problem in the edge computing resource allocation market. Aiming at the problem of resource allocation in edge computing, this paper proposes a double auction scheme-DPODA. This scheme establishes the auction market of edge computing resources, which can improve the social welfare of auction to the maximum extent. Moreover, as the private information of users in the auction market may affect market transactions, in order to solve this problem, this paper uses differential privacy technology to effectively protect the privacy of both sides of the auction, so as to avoid the unfairness in the auction market. A large number of simulation experiments show that the algorithm proposed in this paper not only achieves the research purpose but also ensures the privacy information security of the auctioneer. CCS CONCEPTS • Security and privacy Human and societal aspects of security and privacy; • Networks Cloud computing; • Humancentered computing Ubiquitous and mobile computing; • Theory of computatio Online algorithms;

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“…There is also a line of work on auctions with differential privacy, such as [9,31,39]. However, differential privacy aims to protect privacy from analyzing published output by adding noise to the original output, which is quite different from secure multiparty computation fundamentally.…”

Section: Secure Cloud Resource Auctionmentioning

confidence: 99%

Secure Computation Framework for Multiple Data Providers Against Malicious Adversaries

Chen¹,

Chen²

2020

Preprint

Self Cite

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Due to the great development of secure multi-party computation, many practical secure computation schemes have been proposed. As an example, different secure auction mechanisms have been widely studied, which can protect bid privacy while satisfying various economic properties. However, as far as we know, none of them solve the secure computation problems for multiple data providers (e.g., secure cloud resource auctions) in the malicious security model. In this paper, we use the techniques of cut-andchoose and garbled circuits to propose a general secure computation framework for multiple data providers against malicious adversaries. Specifically, our framework checks input consistency with the cut-and-choose paradigm, conducts maliciously secure computations by running two independent garbled circuits, and verifies the correctness of output by comparing two versions of outputs. Theoretical analysis shows that our framework is secure against a malicious computation party, or a subset of malicious data providers. Taking secure cloud resource auctions as an example, we implement our framework. Extensive experimental evaluations show that the performance of the proposed framework is acceptable in practice. CCS CONCEPTS• Security and privacy → Privacy-preserving protocols; Security protocols.

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Differentially Private Double Spectrum Auction With Approximate Social Welfare Maximization

Cited by 33 publications

References 29 publications

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

DPODA: Differential Privacy-based Online Double Auction for Pervasive Edge Computing Resource Allocation

Secure Computation Framework for Multiple Data Providers Against Malicious Adversaries

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