Paper 2019/819

Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Asma Aloufi, Peizhao Hu, Harry W. H. Wong, and Sherman S. M. Chow


Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems. Recent works propose how to privately evaluate a decision tree in a two-party setting where the feature vector of the client or the decision tree model (such as the threshold values of its nodes) is kept a secret from another party. However, these works cannot be extended trivially to support the outsourcing setting where a third-party who should not have access to the model or the query. Furthermore, their use of an interactive comparison protocol does not support branching program, hence requires interactions with the client to determine the comparison result before resuming the evaluation task. In this paper, we propose the first secure protocol for collaborative evaluation of random forests contributed by multiple owners. They outsource evaluation tasks to a third-party evaluator. Upon receiving the client’s encrypted inputs, the cloud evaluates obliviously on individually encrypted random forest models and calculates the aggregated result. The system is based on our new secure comparison protocol, secure counting protocol, and a multi-key somewhat homomorphic encryption on top of symmetric-key encryption. This allows us to reduce communication overheads while achieving round complexity lower than existing work.

Note: (Author's version)

Available format(s)
Publication info
Preprint. Minor revision.
Applied CryptographyDecision TreeHomomorphic EncryptionMachine LearningRandom Forest
Contact author(s)
ama9000 @ rit edu
2019-07-16: received
Short URL
Creative Commons Attribution


      author = {Asma Aloufi and Peizhao Hu and Harry W.  H.  Wong and Sherman S.  M.  Chow},
      title = {Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption},
      howpublished = {Cryptology ePrint Archive, Paper 2019/819},
      year = {2019},
      note = {\url{}},
      url = {}
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