Securing private data sharing in multi-party analytics
DOI:
https://doi.org/10.5210/fm.v21i9.6842Keywords:
privacy, data sharing, multiparty analyticsAbstract
A general class of problems arises when datasets containing private information belong to multiple parties or owners and they collectively want to perform analytic studies on the entire set while respecting the privacy and security concerns of each individual party. We describe a solution to this problem in the form of a secure procedure for data mapping and/or linkage, which allows to identify the correspondence between entities in a distributed dataset. In contrast to existing methods this solution does not require either a trusted or semi-trusted third party, while being simple, efficient and scalable for both large datasets and number of parties.
Downloads
Published
How to Cite
Issue
Section
License
Authors retain copyright to their work published in First Monday. Please see the footer of each article for details.