Buying private data without verification

Ghosh, Aritra; Ligett, Katrina; Roth, Aaron; Schoenebeck, Grant

doi:10.1145/2600057.2602902

Cited by 57 publications

(44 citation statements)

References 25 publications

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“…(ii) As we show, in this model it is possible to quantify the B's trade-off between an -change in the belief and the cost that B pays A. In that aspect, this model gives a clear quantifiable trade-off that explains what each additional unit of -differential privacy buys B. Interestingly, proper scoring rules were recently applied in the context of differential privacy [12] (yet in a very different capacity).…”

Section: The Coupon Game With Scoring Rules Paymentsmentioning

confidence: 95%

Privacy Games

Sheffet

Vadhan

2014

Web and Internet Economics

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Abstract. The problem of analyzing the effect of privacy concerns on the behavior of selfish utility-maximizing agents has received much attention lately. Privacy concerns are often modeled by altering the utility functions of agents to consider also their privacy loss [24,13,19,4]. Such privacy aware agents prefer to take a randomized strategy even in very simple games in which non-privacy aware agents play pure strategies. In some cases, the behavior of privacy aware agents follows the framework of Randomized Response, a well-known mechanism that preserves differential privacy. Our work is aimed at better understanding the behavior of agents in settings where their privacy concerns are explicitly given. We consider a toy setting where agent A, in an attempt to discover the secret type of agent B, offers B a gift that one type of B agent likes and the other type dislikes. As opposed to previous works, B's incentive to keep her type a secret isn't the result of "hardwiring" B's utility function to consider privacy, but rather takes the form of a payment between B and A. We investigate three different types of payment functions and analyze B's behavior in each of the resulting games. As we show, under some payments, B's behavior is very different than the behavior of agents with hardwired privacy concerns and might even be deterministic. Under a different payment we show that B's BNE strategy does fall into the framework of Randomized Response.

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Section: The Coupon Game With Scoring Rules Paymentsmentioning

confidence: 95%

Privacy Games

Sheffet

Vadhan

2014

Web and Internet Economics

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“…Another line of research examines data acquisition using differential privacy [Ghosh and Roth, 2011, Fleischer and Lyu, 2012, Ghosh et al, 2014, Nissim et al, 2014, Cummings et al, 2015. Agents care about their privacy and hence may be reluctant to reveal their data.…”

Section: Other Related Workmentioning

confidence: 99%

Prior-free Data Acquisition for Accurate Statistical Estimation

Zheng

2019

Proceedings of the 2019 ACM Conference on Economics and Computation

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We study a data analyst's problem of acquiring data from self-interested individuals to obtain an accurate estimation of some statistic of a population, subject to an expected budget constraint. Each data holder incurs a cost, which is unknown to the data analyst, to acquire and report his data. The cost can be arbitrarily correlated with the data. The data analyst has an expected budget that she can use to incentivize individuals to provide their data. The goal is to design a joint acquisition-estimation mechanism to optimize the performance of the produced estimator, without any prior information on the underlying distribution of cost and data. We investigate two types of estimations: unbiased point estimation and confidence interval estimation.Unbiased estimators: We design a truthful, individually rational, online mechanism to acquire data from individuals and output an unbiased estimator of the population mean when the data analyst has no prior information on the cost-data distribution and individuals arrive in a random order. The performance of this mechanism matches that of the optimal mechanism, which knows the true cost distribution, within a constant factor. The performance of an estimator is evaluated by its variance under the worst-case cost-data correlation. Confidence intervals:We characterize an approximately optimal (within a factor 2) mechanism for obtaining a confidence interval of the population mean when the data analyst knows the true cost distribution at the beginning. This mechanism is efficiently computable. We then design a truthful, individually rational, online algorithm that is only worse than the approximately optimal mechanism by a constant factor. The performance of an estimator is evaluated by its expected length under the worst-case cost-data correlation.

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“…A is monotone non-increasing (12) We can now establish the following continuous variant of Theorem 1, which describes the optimal survey mechanism for continuous cost distributions.…”

Section: Continuous Costs For Moment Estimationmentioning

confidence: 99%

Optimal Data Acquisition for Statistical Estimation

Immorlica

Lucier

Syrgkanis

et al. 2018

Proceedings of the 2018 ACM Conference on Economics and Computation

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We consider a data analyst's problem of purchasing data from strategic agents to compute an unbiased estimate of a statistic of interest. Agents incur private costs to reveal their data and the costs can be arbitrarily correlated with their data. Once revealed, data are verifiable. This paper focuses on linear unbiased estimators. We design an individually rational and incentive compatible mechanism that optimizes the worst-case mean-squared error of the estimation, where the worst-case is over the unknown correlation between costs and data, subject to a budget constraint in expectation. We characterize the form of the optimal mechanism in closed-form. We further extend our results to acquiring data for estimating a parameter in regression analysis, where private costs can correlate with the values of the dependent variable but not with the values of the independent variables. Full Paper: https://arxiv.org/abs/1711.01295

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Buying private data without verification

Cited by 57 publications

References 25 publications

Privacy Games

Privacy Games

Prior-free Data Acquisition for Accurate Statistical Estimation

Optimal Data Acquisition for Statistical Estimation

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