Trading privacy through randomized response

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Conference Proceeding

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Personal information is valuable for organizations and companies providing targeted and customized services. On the other hand, data owners are not willing to share their private information (e.g., spending habits and monthly purchases) due to privacy concerns. In order to incentivize the data owners to share their data, the organizations have to pay each data owner adequate compensation. In this context, privacy can be considered a personal commodity: the data owners may share their personal information if the organizations pay them sufficiently. In this paper, we consider a mechanism design problem between a data buyer (e.g., a company) and multiple data owners. In the mechanism design problem, the buyer is willing to make a payment for a desired level of accuracy and data quality, and the data owners will release their information after receiving a sufficient amount of compensation. To measure the privacy guarantee of an algorithm, we use the concept of differential privacy and use the randomized response algorithm to generate differentially private data. In contrast to existing works that study a mechanism design problem for generating a single differentially private linear query through the Laplace mechanism, we consider a more general scenario. In particular, the number of queries or the type of query does not affect our incentive mechanism, and our framework is able to generate any type of query without any limitation on how many times the buyer asks for a query.

Publication Title

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

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