Trading off data resource availability and privacy preservation in multi-layer network transaction

Abstract

The big data market solves the problem of the effective utilization of data through treating data as the circulating commodity in the market. The existing body of research on the big data market suggests that either improving the availability of published data or protecting sensitive information when trading data s the current mainstream topic. To date, the balancing the availability and privacy of the released dataset is a gradually emerging challenge. Unfortunately, there are few studies that have concentrated on the combination of the two points, which is more in line with the actual trading demands and data interaction patterns. Our paper proposes a novel mechanism called Differential Privacy Data Trading (DPDT) mechanism by introducing the differential privacy into the data trading process. Our DPDT mechanism can meet the actual usage requirements of data consumers for the released dataset while ensuring privacy. In short, the DPDT mechanism balances availability and privacy by generating a private synthetic dataset whose accuracy is determined by the data consumer. It is customized for the big data market by improving appropriate synthetic dataset privacy releasing techniques. In addition, DPDT can calculate the corresponding security payment costs depending on the different accuracy of the released dataset by correlating the accuracy, privacy budget, and payment. We instantiate the DPDT with real-world data and the experimental results verify the proposed mechanism is feasible and robust. Our analysis and discussion results reveal that DPDT achieves the trade-off between availability and privacy of the released dataset during data trading.