Paper 2016/736

Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models based on Pre-Computation

Martine De Cock, Rafael Dowsley, Caleb Horst, Raj Katti, Anderson C. A. Nascimento, Stacey C. Newman, and Wing-Sea Poon


Many data-driven personalized services require that private data of users is scored against a trained machine learning model. In this paper we propose a novel protocol for privacy-preserving classification of decision trees, a popular machine learning model in these scenarios. Our solutions are composed out of building blocks, namely a secure comparison protocol, a protocol for obliviously selecting inputs, and a protocol for evaluating polynomials. By combining some of the building blocks for our decision tree classification protocol, we also improve previously proposed solutions for classification of support vector machines and logistic regression models. Our protocols are information theoretically secure and, unlike previously proposed solutions, do not require modular exponentiations. We show that our protocols for privacy-preserving classification lead to more efficient results from the point of view of computational and communication complexities. We present accuracy and runtime results for 7 classification benchmark datasets from the UCI repository.

Available format(s)
Cryptographic protocols
Publication info
Preprint. MINOR revision.
Contact author(s)
rafael dowsley @ kit edu
2017-03-05: last of 2 revisions
2016-07-28: received
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Short URL
Creative Commons Attribution


      author = {Martine De Cock and Rafael Dowsley and Caleb Horst and Raj Katti and Anderson C.  A.  Nascimento and Stacey C.  Newman and Wing-Sea Poon},
      title = {Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models based on Pre-Computation},
      howpublished = {Cryptology ePrint Archive, Paper 2016/736},
      year = {2016},
      note = {\url{}},
      url = {}
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