  Very recently, we are witnessing the emergence of a number of start-ups that
enables individuals to sell their private data directly to brokers and
businesses. While this new paradigm may shift the balance of power between
individuals and companies that harvest data, it raises some practical,
fundamental questions for users of these services: how they should decide which
data must be vended and which data protected, and what a good deal is. In this
work, we investigate a mechanism that aims at helping users address these
questions. The investigated mechanism relies on a hard-privacy model and allows
users to share partial or complete profile data with broker companies in
exchange for an economic reward. The theoretical analysis of the trade-off
between 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 on
some important examples of Bregman divergences. We find a parametric solution
to the problem of optimal exchange of privacy for money, and obtain a
closed-form expression and characterize the trade-off between
profile-disclosure risk and economic reward for several interesting cases.
