Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output

Machine learning is often conducted on datasets containing sensitive information, such as medical records, commercial data, etc. The benefit of learning from such data is tremendous. But when releasing sensitive data, one must take privacy into consideration, and has to tradeoff between the accuracy and the amount of privacy loss of the individuals in the database. In this paper we study differential privacy [11], which has become a standard concept of privacy. Differential privacy guarantees that almost nothing new can be learned from the database that contains one specific individual's information compared with that from the database without that individual's information. More concretely, a mechanism which releases information about the database is said to preserve differential privacy, if the change of a single database element does not affect the probability distribution of the output significantly. Therefore differential privacy provides strong guarantees against attacks; the risk of any individual to submit her information to the database is very small.