Conducting truthful surveys, cheaply

Roth, Aaron; Schoenebeck, Grant

doi:10.1145/2229012.2229076

Cited by 69 publications

(89 citation statements)

References 8 publications

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“…The "sensitive surveyor's problem" [Ghosh and Roth 2013] attempts to model the problem of procuring data from individuals, each with a cost of privacy, in order to estimate some aggregate statistic of a population, and has since been studied in some depth [Ghosh and Roth 2013;Fleischer and Lyu 2012;Roth and Schoenebeck 2012;Ligett and Roth 2012;Ghosh and Ligett 2013;Nissim et al 2014]. This stream of work, however, has thus far only modeled settings in which the data collected from each participant are verifiable, and the only information that a participant can misreport is her privacy cost determining the compensation she requires for participation-that is, an individual may choose to not participate in the survey, but if participating, she cannot lie about her data.…”

Section: Introductionmentioning

confidence: 99%

Buying private data without verification

Ghosh

Ligett

Roth

et al. 2014

Proceedings of the Fifteenth ACM Conference on Economics and Computation

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We consider the problem of designing a survey to aggregate non-verifiable information from a privacy-sensitive population: an analyst wants to compute some aggregate statistic from the private bits held by each member of a population, but cannot verify the correctness of the bits reported by participants in his survey. Individuals in the population are strategic agents with a cost for privacy, i.e., they not only account for the payments they expect to receive from the mechanism, but also their privacy costs from any information revealed about them by the mechanism's outcome-the computed statistic as well as the payments-to determine their utilities. How can the analyst design payments to obtain an accurate estimate of the population statistic when individuals strategically decide both whether to participate and whether to truthfully report their sensitive information?We design a differentially private peer-prediction mechanism [Miller et al. 2005] that supports accurate estimation of the population statistic as a Bayes-Nash equilibrium in settings where agents have explicit preferences for privacy. The mechanism requires knowledge of the marginal prior distribution on bits b i , but does not need full knowledge of the marginal distribution on the costs c i , instead requiring only an approximate upper bound. Our mechanism guarantees -differential privacy to each agent i against any adversary who can observe the statistical estimate output by the mechanism, as well as the payments made to the n − 1 other agents j = i. Finally, we show that with slightly more structured assumptions on the privacy cost functions of each agent [Chen et al. 2013], the cost of running the survey goes to 0 as the number of agents diverges.

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Section: Introductionmentioning

confidence: 99%

Buying private data without verification

Ghosh

Ligett

Roth

et al. 2014

Proceedings of the Fifteenth ACM Conference on Economics and Computation

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show abstract

“…In this work, as well as Roth and Schoenebeck [2012] and Chen et al [2018a], agents do not derive utility or disutility from the estimation outcome, cannot fabricate their data, and have a cost for revealing their data. The mechanism uses payment to incentivize data revelation.…”

Section: Other Related Workmentioning

confidence: 99%

“…The problem of purchasing data for unbiased estimation of population mean was first formulated by Roth and Schoenebeck [2012] and then further studied by Chen et al [2018a]. Both works however assume that the cost distribution is known to the analyst and aim at obtaining an optimal unbiased estimator with minimum worst-case variance for population mean, where the worst-case is over all data-cost distributions consistent with the known cost distribution, subject to an expected budget constraint.…”

Section: Introductionmentioning

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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“…Their mechanism uses these pricing functions to compute a value of ε and payment values that are paid out to each player such that enough of them are incentivized to participate to make the outcome of the sanitizer accurate. More follow-up works in this direction have appeared recently [32,17,25]. The main difference with our model is that in this line of work, it is assumed that the players' private information will be accurately reported, and they may only lie about how much they value their privacy.…”

Section: Previous Workmentioning

confidence: 99%

Is privacy compatible with truthfulness?

Xiao

2013

Proceedings of the 4th Conference on Innovations in Theoretical Computer Science

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In the area of privacy-preserving data mining, a differentially private mechanism intuitively encourages people to share their data because they are at little risk of revealing their own information. However, we argue that this interpretation is incomplete because external incentives are necessary for people to participate in databases, and so data release mechanisms should not only be differentially private but also compatible with incentives, otherwise the data collected may be false. We apply the notion of truthfulness from game theory to this problem. In certain settings, it turns out that existing differentially private mechanisms do not encourage participants to report their information truthfully.On the positive side, we exhibit a transformation that takes truthful mechanisms and transforms them into differentially private mechanisms that remain truthful. Our transformation applies to games where the type space is small and the goal is to optimize an insensitive quantity such as social welfare. Our transformation incurs only a small additive loss in optimality, and it is computationally efficient. Combined with the VCG mechanism, our transformation implies that there exists a differentially private, truthful, and approximately efficient mechanism for any social welfare game with small type space.We also study a model where an explicit numerical cost is assigned to the information leaked by a mechanism. We show that in this case, even differential privacy may not be strong enough of a notion to motivate people to participate truthfully. We show that mechanisms that release a perturbed histogram of the database may reveal too much information. We also show that, in general, any mechanism that outputs a synopsis that resembles the original database (such as the mechanism of Blum et al. (STOC '08)) may reveal too much information.

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Conducting truthful surveys, cheaply

Cited by 69 publications

References 8 publications

Buying private data without verification

Buying private data without verification

Prior-free Data Acquisition for Accurate Statistical Estimation

Is privacy compatible with truthfulness?

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