Privacy Auctions for Recommender Systems

Dandekar, Pranav; Fawaz, Nadia; Ioannidis, Stratis

doi:10.1007/978-3-642-35311-6_23

Cited by 25 publications

(27 citation statements)

References 14 publications

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“…The example in this section of using differential privacy to obtain exactly truthful mechanisms is taken directly from [69]. The sensitive surveyors problem was first considered by Ghosh and Roth [36], and expanded on by [56,34,75,16]. Fleischer and Lyu [34] consider the Bayesian setting discussed in this section, and Ligett and Roth [56] consider the worst-case setting with take-it-or-leave-it offers, both in an attempt to get around the impossibility result of [36].…”

Section: Bibliographical Notesmentioning

confidence: 99%

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The Algorithmic Foundations of Differential Privacy

Dwork

Roth

2013

FNT in Theoretical Computer Science

4,433

5,103

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The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition.After motivating and discussing the meaning of differential privacy, the preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some astonishingly powerful computational results, there are still fundamental limitationsnot just on what can be achieved with differential privacy but on what can be achieved with any method that protects against a complete breakdown in privacy. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power. Certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed.We then turn from fundamentals to applications other than queryrelease, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams is discussed. Basic TermsThis section motivates and presents the formal definition of differential privacy, and enumerates some of its key properties. Formalizing differential privacyWe will think of databases x as being collections of records from a universe X . It will often be convenient to represent databases by their histograms: x ∈ N |X | , in which each entry x i represents the number of elements in the database x of type i ∈ X (we abuse notation slightly, letting the symbol N denote the set of all non-negative integers, including zero). In this representation, a natural measure of the distance between two databases x and y will be their 1 distance: Definition 2.3 (Distance Between Databases). The 1 norm of a database x is denoted x 1 and is defined to be:The 1 distance between two databases x and y is x − y 1 Note that x 1 is a measure of the size of a database x (i.e., the number of records it contains), and x − y 1 is a measure of how many records differ between x and y.Databases may also be represented by multisets of rows (elements of X ) or even ordered lists of rows, which is a special case o...

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Section: Bibliographical Notesmentioning

confidence: 99%

“…Algorithm16 The Multiplicative Weights (or Randomized Weighted Majority (RWM)) algorithm, rephrased. It takes as input a stream of losses 1 , 2 , .…”

mentioning

confidence: 99%

The Algorithmic Foundations of Differential Privacy

Dwork

Roth

2013

FNT in Theoretical Computer Science

4,433

5,103

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“…An alternative notion is that of budget feasible mechanism design initiated by Singer [2010] where the goal is to optimize a utility function under a hard budget constraint. This concept enables surprisingly positive results and has been studied in algorithmic mechanism design [Chen et al 2011;Dobzinski et al 2011;Badanidiyuru et al 2012;Bei et al 2012;Anari et al 2014] and used in various application domains that include social network analysis [Singer 2012], crowdsourcing [Yang et al 2012;Singer and Mittal 2013;Singla and Krause 2013b] and privacy auctions [Dandekar et al 2013;Singla and Krause 2013a]. The setting that we study most resembles that of budget feasible mechanism design.…”

Section: Related Workmentioning

confidence: 90%

Mechanisms for Fair Attribution

Balkanski

Singer

2015

Proceedings of the Sixteenth ACM Conference on Economics and Computation

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We propose a new framework for optimization under fairness constraints. The problems we consider model procurement where the goal is to optimize a buyer's utility while paying sellers in a manner that reflects their contribution to the buyer's utility. The payment rules we consider are natural interpretations of fairness based on concepts such as Shapley values and the nucleolus from cooperative game theory. The question in this setting is whether the outcome (measured in terms of the buyer's utility) produced by mechanisms that enforce fair payments is competitive with the outcome of a mechanism that simply pays agents their costs and is not committed to fair payments. Our main result shows that there exists a mechanism which guarantees a solution whose value is at least one third of the optimal unfair solution for any submodular utility function, and that this ratio is optimal. We discuss several special cases for which this approximation ratio can be improved and natural extensions.

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“…Auctions have been recognized in the literature as an effective way to facilitate exchange of personal information and payment (Preibusch, 2013; Dandekar, Fawaz, & Ioannidis, 2014; Ghosh & Roth 2015). An experimental auction to study the monetary value of people's age and weight information was conducted in Huberman, Adar, and Fine (2005).…”

Section: Related Workmentioning

confidence: 99%

Valuing Personal Data with Privacy Consideration

Motiwalla

2020

Decision Sciences

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A key challenge in information privacy research is how to value personal data with privacy consideration. In this study, we propose an experimental auction approach for valuing personal data. We use the generalized second‐price auction to assess the monetary values of individuals’ identity, demographic, and private information. We find that individuals’ economic valuation of personal data is consistent with their actual self‐disclosure behaviors. The economic valuation approach also produces results that are consistent with some well‐accepted observations about consumer demographics and privacy. On the other hand, individuals’ stated privacy preferences and attitudes are not consistent with their economic valuation. The findings of this study suggest that the proposed approach can be an effective mechanism for measuring personal data privacy. This study also provides important insights into valuing personal information for practical uses with several implications to policy decision makers, corporate executives and managers, data analysts, as well as decision science researchers.

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Privacy Auctions for Recommender Systems

Cited by 25 publications

References 14 publications

The Algorithmic Foundations of Differential Privacy

The Algorithmic Foundations of Differential Privacy

Mechanisms for Fair Attribution

Valuing Personal Data with Privacy Consideration

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