Modern cryptography is known for the introduction of public key cryptography, which has been widely applied in practice. However, the theory of cryptography provided additional powerful (and less intuitive) tools. One of its most attractive contributions is secure computation, also known as secure function evaluation - SFE, which allows multiple participants to implement a joint computation that, in real life, may only be implemented using a trusted party. The participants, each with its own private input, communicate without the help of any trusted party, and can compute any function without revealing any information about the inputs except for the value of the function. A classic example of such a computation is the “millionaires’ problem”, in which two millionaires want to find out who is richer, without revealing their actual worth. Thus far, secure computation techniques have rarely been applied in practice, and are typically considered to have mostly theoretical significance. In this research proposal we intend to build tools that translate these theoretical results into practical applications. Our goal is that secure computation solutions, which today are usually stated as mathematical theorems, will be available as tools usable by non-experts, similar to state-of-the-art tools for technologies such as public key encryption, linear programming, or data compression. The research will proceed in two directions: First, we will develop generic tools (essentially compilers) which translate functions defined using a high-level language to distributed programs that implement secure evaluation of the defined functions. We also expect that this effort will unearth many questions of theoretical interest, which we will investigate. Our other direction of research is the design of specialized, and highly efficient, solutions to key tasks which have conflicting goals of respecting privacy and enabling legitimate usage of data.