Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing

Zheng, Zhenzhe; Peng, Yanqing; Wu, Fan; Tang, Shaojie; Chen, Guihai

doi:10.1109/jsac.2017.2659258

Cited by 67 publications

(19 citation statements)

References 36 publications

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“…In [26], a 'double or nothing' incentivecompatible mechanism is proposed to ensure workers behave honesty based on their self-confidence; this protocol is provable to avoid spammers from the crowd, under the assumption that every worker wants to maximize their expected payment. In [40,39,41,42,44,43], Zheng et al, the authors leverage the tools of game theory and mechanism design to analyze the interaction of rational and selfish mobile users, then design efficient incentive mechanisms for four classical and representative applications in mobile Internet: dynamic spectrum redistribution, mobile crowdsensing, data marketplace, and cloud bandwidth management, to stimulate selfish mobile users to cooperate, achieving a win-win situation.…”

Section: Mechanism Design In Crowdsourcingmentioning

confidence: 99%

A Brief Survey of Data Pricing for Machine Learning

Tang¹,

Lv²,

Wu³

2020

8th International Conference on Signal, Image Processing and Pattern Recognition

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Big data and machine learning are poised to revolutionize the field of artificial intelligence and represent a step towards building an intelligent society. Big data is considered to be the key to unlocking the next great waves of growth in productivity, the value of data is realized through machine learning. In this survey, we begin with an introduction to the general field of data pricing and distributed machine learning then progress to the main streams of data pricing and mechanism design methods. Our survey will cover several current areas of research within the field of data pricing, including the incentive mechanism design for federated learning, reinforcement learning, auction, crowdsourcing, and blockchain, especially, focus on reward function for machine learning and payment scheme. In parallel, we highlight the pricing scheme in data transactions, focusing on data evaluation via distributed machine learning. To conclude, we discuss some research challenges and future directions of data pricing for machine learning.

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Section: Mechanism Design In Crowdsourcingmentioning

confidence: 99%

A Brief Survey of Data Pricing for Machine Learning

Tang¹,

Lv²,

Wu³

2020

8th International Conference on Signal, Image Processing and Pattern Recognition

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“…The valuations are obtained using different generative random processes, so as to observe the algorithmic behavior under different scenarios. These generative models are motivated by studies modeling valuations for digital goods in online platforms and their pricing [43,18,34,45,47,13,48].…”

Section: Experimental Evaluationmentioning

confidence: 99%

Revenue maximization for query pricing

et al. 2019

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Buying and selling of data online has increased substantially over the last few years. Several frameworks have already been proposed that study query pricing in theory and practice. The key guiding principle in these works is the notion of arbitrage-freeness where the broker can set different prices for different queries made to the dataset, but must ensure that the pricing function does not provide the buyers with opportunities for arbitrage. However, little is known about revenue maximization aspect of query pricing. In this paper, we study the problem faced by a broker selling access to data with the goal of maximizing her revenue. We show that this problem can be formulated as a revenue maximization problem with single-minded buyers and unlimited supply, for which several approximation algorithms are known. We perform an extensive empirical evaluation of the performance of several pricing algorithms for the query pricing problem on real-world instances. In addition to previously known approximation algorithms, we propose several new heuristics and analyze them both theoretically and experimentally. Our experiments show that algorithms with the best theoretical bounds are not necessarily the best empirically. We identify algorithms and heuristics that are both fast and also provide consistently good performance when valuations are drawn from a wide variety of distributions.

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“…After encryption, every datum donor oi registers the signature δi on the cipher text vector ~Di utilizing her mystery key: Inevitably, oi presents her tuple ( PIDi; ~Di; δi ) to the specialist organization. Then again, to limit an enlisted data giver from utilizing a similar pair of pseudo identity and mystery key for various occasions in various sessions of data procurement [15], one inborn route is to typify the signing stage into the carefully designed gadget. However, another practical route is to give the specialist organization a chance to store those utilized pseudo personalities for duplication check later.…”

Section: Encrypted Data Signingmentioning

confidence: 99%

Accomplishing Data Integrity and Confidentiality in Data Markets

Gadiraju¹,

Vemulavada²,

Atcha³

et al. 2019

IJITEE

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As a noteworthy business worldview, a few on-line information stages have developed to fulfill society's wants for individual explicit learning, any place a service provider assembles raw data from data givers, at that point offers data services to data clients. Notwithstanding, inside the data exchanging level, the data customers face a squeezing issue, i.e., an approach to confirm whether the service provider has actually gathered and handled data. During this paper, we propose TPDM, that effectively compose truthfulness and Privacy protection in data Markets. TPDM is structured inside in partner degree Encrypt-then-Sign way; utilize mostly homomorphism encryption and identity-based signature. It along encourage bunch confirmation, processing, and result check, though giving identity protection and data confidentiality. We used dataset and 2015 RECS dataset, severally. Our examination and investigation results that TPDM accomplishes numerous alluring properties, though obtaining low calculation and correspondence overheads once sustaining huge size data markets

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Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing

Cited by 67 publications

References 36 publications

A Brief Survey of Data Pricing for Machine Learning

A Brief Survey of Data Pricing for Machine Learning

Revenue maximization for query pricing

Accomplishing Data Integrity and Confidentiality in Data Markets

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