The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies. The massive collection and usage of data, however,pose an increasing risk for people's private and sensitive informationdue to either inadvertent mishandling or malicious exploitation.Besides legislative solutions,many technical approaches have been proposed towards data privacy protection.However, they bear various limitations such as leading to degraded data availability and utility, or relying on heuristics and lacking solid theoretical bases.To overcome these limitations,we propose a formal information-theoretic definition for this utility-preserving privacy protection problem,and design a data-driven learnable data transformation frameworkthat is capable of selectively suppressing sensitive attributes from target datasets while preserving the other useful attributes,regardless of whether or not they are known in advance or explicitly annotated for preservation.We provide rigorous theoretical analyses on the operational bounds for our framework,and carry out comprehensive experimental evaluations using datasets of a variety of modalities,including facial images, voice audio clips, and human activity motion sensor signals.Results demonstrate the effectiveness and generalizability of our method under various configurations on a multitude of tasks.