Optimized, Direct Sale of Privacy in Personal-Data Marketplaces
This addresses a practical issue for users of emerging personal-data marketplaces, though it is incremental as it builds on existing privacy models.
The paper tackles the problem of how individuals should decide which personal data to sell and at what price in direct-sale marketplaces, finding a parametric solution and closed-form expression for the optimal trade-off between privacy disclosure risk and economic reward.
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.