Very recently, we are witnessing the emergence of a number of start-ups thatenables individuals to sell their private data directly to brokers andbusinesses. While this new paradigm may shift the balance of power betweenindividuals and companies that harvest data, it raises some practical,fundamental questions for users of these services: how they should decide whichdata must be vended and which data protected, and what a good deal is. In thiswork, we investigate a mechanism that aims at helping users address thesequestions. The investigated mechanism relies on a hard-privacy model and allowsusers to share partial or complete profile data with broker companies inexchange for an economic reward. The theoretical analysis of the trade-offbetween privacy and money posed by such mechanism is the object of this work.We adopt a generic measure of privacy although part of our analysis focuses onsome important examples of Bregman divergences. We find a parametric solutionto the problem of optimal exchange of privacy for money, and obtain aclosed-form expression and characterize the trade-off betweenprofile-disclosure risk and economic reward for several interesting cases.
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