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Health informatics, using new information technology, provides a fruitful set of data resource and knowledge that is very useful for secondary data users and researchers in various health systems and applications. Privacy acts, on the other hand, prevent direct access to this information without patient's consent. Various solutions have been proposed such as data anonymization and de-identification, on-site analysis, and limited remote access, to preserve the data owner's privacy. Each of those approaches has different drawbacks and limitations. For instance, data de-identification will reduce data utility because of low precision of the final released data, and also it has a risk of data re-identification using available public data and background knowledge. On-site analysis has physical limitations, such as lack of data centers in every geographic area, and time-consuming procedures, such as background checks. Remote access increases security risks, and when data has to be pulled from multiple data resources, it requires patient consent for data disclosure. In this paper, we propose a set of privacy-preserving methods and techniques for some popular health statistical analysis methods. Using this set of secure protocols health researchers and other data users are able to issue their requests as some queries, and receive only the results of their queries from the data owners, while each data custodian can keep their sensitive data private. Proposed methods have been tested using sample data to illustrate the performance of the results in terms of computational and communication complexities. Security proof of the proposed protocols has also been provided as a proof of concept. [ABSTRACT FROM AUTHOR]