In privacy-enhancing technology, it has been inevitably challenging to strike a maintain balance between privacy, efficiency and usability (utility). We propose a highly practical and efficient approach for privacy-preserving integration and sharing of datasets among a group of participants. At the heart of our solution is a new interactive protocol, Secure Channel. Through Secure Channel, each participant is able to randomize their datasets via an independent and untrusted third party, such that the resulting dataset can be merged with other randomized datasets contributed by other participants group in a privacy-preserving manner. Our process does not require any public or key sharing between participants in order to integrate different datasets. This, in turn, leads to a user can understand and use easily and scalable solution. Moreover, the accuracy of a randomized dataset which are returned by the third party can be securely verified by the other participant of group. We further demonstrate Secure Channel’s general utilities, using it to construct a structure preserving data integration protocol. This is mainly useful for, good quality integration of network traffic data.
Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method
