Privacy Bargaining with Fairness: Privacy-Price Negotiation System for Applying Differential Privacy in Data Market Environments

Digital data are an essential resource for intelligent decision making. As the value of digital data increases, digital markets, where data owner and consumers can deal with data, have also been attracting attention as a means to obtain data. However, the collection of digital data can lead to privacy breaches, which are a substantial impediment that hinders an individual's willingness to provide data. Differential privacy, which is a de facto standard for privacy protection in statistical databases, can be applied to solve the privacy violation problem. To apply differential privacy to the data market, the amount of noise and corresponding data price must be determined; however, this matter has not yet been studied. In this work, we propose a fair negotiation method that can set the appropriate price and noise parameter in the differentially private data market environment. We suggest a data market framework with a market manager that acts as a broker between the data provider and consumer. We also propose a negotiation technique to determine the data price and noise parameter epsilon using Rubinstein bargaining considering social welfare to prevent unfair transactions. We validate that the proposed negotiation technique can determine an appropriate level of epsilon and unit price without unfair trade to either the data provider and the consumer.