Securing private data sharing in multi-party analytics


  • Gowtham Bellala Hewlett Packard Laboratories
  • Bernardo Huberman Hewlett Packard Laboratories



privacy, data sharing, multiparty analytics


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.




How to Cite

Bellala, G., & Huberman, B. (2016). Securing private data sharing in multi-party analytics. First Monday, 21(9).