In this abstract, we present an algorithm for differentially private data release for vertically partitioned data between two parties. Additionally, the proposed algorithm satisfies the security definition of the semi honest adversary model. In this model, parties follow the algorithm but may try to deduce additional information from the received messages. Therefore, at any time during the execution of the algorithm, no party should learn more information about the other party’s data than what is found in the final integrated table, which is differentially private. We present a two-party protocol for the exponential mechanism. We use this protocol as a sub protocol of our main algorithm, and it can also be used by any other algorithm that uses the exponential mechanism in a distributed setting. We present the first two – party data publishing algorithm for vertically partitioned data that generate an integrated data table satisfying differential privacy. The algorithm also satisfies the security definition in the secure multi party computation (SMC) literature. Experimental results on real-life data suggest that the proposed algorithm can effectively preserve information for a data mining task.
You are here: / / DIFFERENTIALLY PRIVATE DATA RELEASE FOR VERTICALLY PARTITIONED DATA BETWEEN TWO PARTIES