Data outsourcing or database as a service is a new paradigm for data management in which a third party service provider hosts a database as a service. The service provides data management for its customers and thus obviates the need for the service user to purchase expensive hardware and software, deal with software upgrades and hire professionals for administrative and maintenance tasks. Since using an external database service promises reliable data storage at a low cost it is very attractive for companies. Such a service would also provide universal access, through the Internet to private data stored at reliable and secure sites in cloud computing infrastructures. However, recent governmental legislations, competition among companies, and data thefts mandate companies to use secure and privacy preserving data management techniques. The data provider, therefore, needs to guarantee that the data is secure, be able to execute queries on the data, and the results of the queries must also be secure and not visible to the data provider. Current research has been focused only on how to index and query encrypted data. However, querying encrypted data is computationally very expensive. Providing an efficient trust mechanism to push both database service providers and clients to behave honestly has emerged as one of the most important problem before data outsourcing to become a viable paradigm. In this paper, we describe scalable privacy preserving algorithms for data outsourcing in cloud computing infrastructures. Instead of encryption, which is computationally expensive, we use distribution on multiple sites that are available in the cloud and information theoretically proven secret sharing algorithms as the basis for privacy preserving outsourcing. The technical contributions of this paper is the establishment and development of a framework for efficient fault-tolerant scalable and theoretically secure privacy preserving data outsourcing that supports a diversity of database operations executed on different types of data.