Option contracts for a privacy-aware market

Suppliers (including companies and individual prosumers) may wish to protect their private information when selling items they have in stock. A market is envisaged where private information can be protected through the use of differential privacy and option contracts, while privacy-aware suppliers deliver their stock at a reduced price. In such a marketplace a broker acts as intermediary between privacy-aware suppliers and end customers, providing the extra items possibly needed to fully meet the customers' demand, while end customers book the items they need through an option contract. All stakeholders may benefit from such a marketplace. A formula is provided for the option price, and a budget equation is set for the mechanism to be profitable for the broker/producer

Differential Privacy: An Estimation Theory-Based Method for Choosing Epsilon

Differential privacy is achieved by the introduction of Laplacian noise in the response to a query, establishing a precise trade-off between the level of differential privacy and the accuracy of the database response (via the amount of noise introduced). However, the amount of noise to add is typically defined through the scale parameter of the Laplace distribution, whose use may not be so intuitive. In this paper we propose to use two parameters instead, related to the notion of interval estimation, which provide a more intuitive picture of how precisely the true output of a counting query may be gauged from the noise-polluted one (hence, how much the individual's privacy is protected)