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Machine learning algorithms have reached mainstream status and are widelydeployed in many applications. The accuracy of such algorithms dependssignificantly on the size of the underlying training dataset; in reality asmall or medium sized organization often does not have the necessary data totrain a reasonably accurate model. For such organizations, a realistic solutionis to train their machine learning models based on their joint dataset (whichis a union of the individual ones). Unfortunately, privacy concerns preventthem from straightforwardly doing so. While a number of privacy-preservingsolutions exist for collaborating organizations to securely aggregate theparameters in the process of training the models, we are not aware of any workthat provides a rational framework for the participants to precisely balancethe privacy loss and accuracy gain in their collaboration. In this paper, byfocusing on a two-player setting, we model the collaborative training processas a two-player game where each player aims to achieve higher accuracy whilepreserving the privacy of its own dataset. We introduce the notion of Price ofPrivacy, a novel approach for measuring the impact of privacy protection on theaccuracy in the proposed framework. Furthermore, we develop a game-theoreticalmodel for different player types, and then either find or prove the existenceof a Nash Equilibrium with regard to the strength of privacy protection foreach player. Using recommendation systems as our main use case, we demonstratehow two players can make practical use of the proposed theoretical framework,including setting up the parameters and approximating the non-trivial NashEquilibrium.

Together or Alone: The Price of Privacy in Collaborative Learning