Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

Fallah, Alireza; Makhdoumi, Ali; Malekian, Azarakhsh; Ozdaglar, Asuman

doi:10.48550/arxiv.2201.03968

Cited by 2 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fallah et al [24] considered building a platform that collects data from privacysensitive users and formulated the problem as a Bayesian optimal mechanism design. Individuals could share their (verifiable) data in exchange for monetary rewards or services.…”

Section: Privacy-based Pricing Modelsmentioning

confidence: 99%

A Survey on Data Pricing: Methods, Challenges, and Prospects

Chi,

Zhao,

Fang

et al. 2023

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

As organizations increasingly rely on data-driven decision-making, understanding the true value and potential of data becomes crucial. Data pricing, which aims to determine the financial value of information, plays a pivotal role in data circulation and transactions. However, the existing surveys on data pricing have limitations that need to be addressed. This paper presents a comprehensive survey of big data pricing methods from a data science perspective. It begins by providing an overview of the fundamental concepts underlying data pricing and the data market. Subsequently, it delves into the general principles and challenges associated with data pricing. The survey categorizes and summarizes various approaches and methods employed in data pricing, assessing their respective advantages and disadvantages. In conclusion, this paper identifies potential research directions to enhance our understanding of data pricing. By rectifying the deficiencies in existing surveys, this comprehensive study aims to contribute to the development of effective data pricing strategies and foster advancements in the field of data science.

show abstract

Section: Privacy-based Pricing Modelsmentioning

confidence: 99%

A Survey on Data Pricing: Methods, Challenges, and Prospects

Chi,

Zhao,

Fang

et al. 2023

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

“…Start from [18], there is a long list of work studies data acquisition from agents that have privacy concern from different perspectives [27,16,29,17,14]. However, most of them do not consider statistical estimation problems.…”

Section: Related Workmentioning

confidence: 99%

“…Considering the failure of the event E and combining ( 13), (14), and (20) delivers that for any δ > 0, with probability at least 1…”

Section: Omitted Proofsmentioning

confidence: 99%

Truthful Generalized Linear Models

Yuan¹,

Liu²,

Wang³

2022

Preprint

View full text Add to dashboard Cite

In this paper we study estimating Generalized Linear Models (GLMs) in the case where the agents (individuals) are strategic or self-interested and they concern about their privacy when reporting data. Compared with the classical setting, here we aim to design mechanisms that can both incentivize most agents to truthfully report their data and preserve the privacy of individuals' reports, while their outputs should also close to the underlying parameter. In the first part of the paper, we consider the case where the covariates are sub-Gaussian and the responses are heavy-tailed where they only have the finite fourth moments. First, motivated by the stationary condition of the maximizer of the likelihood function, we derive a novel private and closed form estimator. Based on the estimator, we propose a mechanism which has the following properties via some appropriate design of the computation and payment scheme for several canonical models such as linear regression, logistic regression and Poisson regression: (1) the mechanism is o(1)-jointly differentially private (with probability at least 1 − o(1)); (2) it is an o( 1 n )-approximate Bayes Nash equilibrium for a (1 − o(1))-fraction of agents to truthfully report their data, where n is the number of agents; (3) the output could achieve an error of o(1) to the underlying parameter; (4) it is individually rational for a (1 − o(1)) fraction of agents in the mechanism ; (5) the payment budget required from the analyst to run the mechanism is o(1). In the second part, we consider the linear regression model under more general setting where both covariates and responses are heavy-tailed and only have finite fourth moments. By using an ℓ 4 -norm shrinkage operator, we propose a private estimator and payment scheme which have similar properties as in the sub-Gaussian case.

show abstract

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

Cited by 2 publications

References 45 publications

A Survey on Data Pricing: Methods, Challenges, and Prospects

A Survey on Data Pricing: Methods, Challenges, and Prospects

Truthful Generalized Linear Models

Contact Info

Product

Resources

About