Brief Announcement: Privacy Preserving Mining of Distributed Data Using a Trusted and Partitioned Third Party

  • Nir Maoz
  • Ehud GudesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10332)


We like to discuss the usability of new architecture of partitioned third party, offered in [1] for conducting a new protocols for data mining algorithms over shared data base between multiple data holders. Current solution for data mining over partitioned data base are: Data anonimization [4], homomorphic encryption [5], trusted third party [2] or secure multiparty computation algorithms [3]. Current solutions suffer from different problems such as expensive algorithms in terms of computation overhead and required communication rounds, revealing private information to third party. The new architecture offered by Sherman et al. allow the data holders to use simple masking techniques that are not expensive in computation nor assume trust in the third party, yet allow to perform simple and complex data mining algorithms between multiple data owners while private data is not revealed. That come with the assumption of no collude between the two parts of the PTTP.


Privacy Preserving Mining Requires Communication Rounds Multiple Data Owners Data-based Partitioning Shared Data Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Tassa Tamir, for providing very helpful comments on the algorithms presented here.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceThe Open UniversityRa’ananaIsrael

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