executing computations on untrusted machines in a trustworthy manner

We present sTile, a technique for building software systems that distribute large computations onto the cloud while providing guarantees that the cloud nodes cannot learn the computation’s private data. sTile is based on a nature-inspired, theoretical model of self-assembly. While sTile’s computational model is Turing universal, in this paper, we present a prototype implementation that solves NP-complete problemsThis paper focuses specifically on NP-complete problems and demonstrates how sTile-based systems can solve important real-world problems, such as protein folding, image recognition, and resource allocation. We present the algorithms involved in sTile and formally prove that sTile-based systems preserve privacy. We develop a reference sTile-based implementation and empirically evaluate it on several physical networks of varying sizes, including the globally distributed PlanetLab testbed. Our analysis demonstrates sTile’s scalability and ability to handle varying network delay, as well as verifies that problems requiring privacypreservation can be solved using sTile orders of magnitude faster than using today’s state-of-the-art alternatives