Privata: Differentially private data market framework using negotiation-based pricing mechanism

Abstract

As the value of digital data increases, the data market is in the spotlight as a means of obtaining a personal information. However, the collection of personal information makes a serious privacy violation and it is a serious problem in the use of personal 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 should be determined between the provider and consumer. However, this matter has not yet been studied. In this work, we introduce a Privata which is a differentially private data market framework to set the appropriate price and noise parameter in the data market environment. The Privata is based on negotiation technique using Rubinstein bargaining considering social welfare to prevent unfair transactions. We explain the Privata overview and negotiation technique in Privata, and show the Privata implementation.