Per-se Privacy Preserving Solution Methods Based on Optimization

Ensuring privacy is an essential requirement in various contexts, such as social networks, healthcare data, e- commerce, banks, and government services. Here, different en- tities coordinate to address specific problems where the sensitive problem data are distributed among the involved entities and no entity wants to publish its data during the solution procedure. Existing privacy preserving solution methods are mostly based on cryptographic procedures and thus have the drawback of substantial computational complexity. Surprisingly, little atten- tion has been devoted thus far to exploit mathematical opti- mization techniques and their inherent properties for preserving privacy. Yet, optimization based approaches to privacy require much less computational effort compared to cryptographic variants, which is certainly desirable in practice. In this paper, a unified framework for transformation based optimization methods that ensure privacy is developed. A general definition for the privacy in the context of transformation methods is proposed. A number of examples are provided to illustrate the ideas. It is concluded that the theory is still in its infancy and that huge benefits can be achieved by a substantial development.