The need for improved segmentation, targeting, personalization fuels the practice of data sharing among companies. Concurrently, data sharing faces the headwind of new laws emphasizing users’ privacy in data. Under the premise that sharing of data occurs from a provider to a recipient, we propose a practicable approach of generating representational data for sharing that achieves value-addition for the recipient’s tasks while preserving privacy of users. Prior art shows that the mechanism to improve value-addition inevitably weakens privacy in the generated data. In a first of a kind contribution, our system offers tunable controls to adjust the extent of privacy desired by the provider and the extent of value-addition expected by the recipient. Our experiments on a public data show that under common organizational practice of data-sharing, data generation for value-addition is achievable while preserving privacy. Our demonstration starkly shows the trade-off between privacy-protection and value addition, through user-controlled knobs and offers a prototype of a platform for data sharing which is mindful of this trade-off.