Abstract
Personal information and other types of private data are valuable for both data owners and institutions interested in providing targeted and customized services that require analyzing such data. In this context, privacy is sometimes seen as a commodity: institutions (data buyers) pay individuals (or data sellers) in exchange for private data. In this study, we examine the problem of designing such data contracts, through which a buyer aims to minimize his payment to the sellers for a desired level of data quality, while the latter aim to obtain adequate compensation for giving up a certain amount of privacy. Specifically, we use the concept of differential privacy and examine a model of linear and nonlinear queries on private data. We show that conventional algorithms that introduce differential privacy via zero-mean noise fall short for the purpose of such transactions as they do not provide a sufficient degree of freedom for the contract designer to negotiate between the competing interests of the buyer and the sellers. Instead, we propose a biased randomized algorithm to generate differentially private output and show that this algorithm allows us to customize the privacy-accuracy tradeoff for each individual. We use a contract design approach to find the optimal contracts when using this biased algorithm to provide privacy and show that under this combination the buyer can achieve the same level of accuracy with a lower payment as compared to using the conventional, unbiased algorithms, while at the same time incurring lower privacy loss for the sellers.
Highlights
Advances in technology and data centers have enabled storing large amounts of data containing private information of individuals or firms
We show that by choosing the bias term carefully, a contract can be designed for the buyer to obtain the desired accuracy level at a lower cost, as compared to when an unbiased algorithm is used, while at the same time the sellers experience less privacy loss
PRELIMINARIES we review the notion of differential privacy first proposed in [8], [22] which we will use to quantify privacy leakage, and introduce a type of linear query
Summary
Advances in technology and data centers have enabled storing large amounts of data containing private information of individuals or firms. We show that by choosing the bias term carefully, a contract can be designed for the buyer to obtain the desired accuracy level at a lower cost, as compared to when an unbiased algorithm is used, while at the same time the sellers experience less privacy loss. We present a new algorithm for generating differentially private estimates of a family of linear and nonlinear queries, and show that this algorithm allows the data broker to assign different privacy losses to different individuals. This algorithm improves the privacy-accuracy tradeoff as compared to the unbiased algorithm, and allows data broker to assign different privacy losses to individuals when the buyer requests a non-linear query.
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