Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

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Product Code: Java - Machine Learning
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Aim:


           Blindfolded Evaluation Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems.


Abstract:


       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 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.


Proposed System:


         Existing privacy-preserving protocols follow the client-server model where the server owns the random forest and the client inputs encrypted features to start the evaluation. Comparison at each node is carried out using the secure comparison protocol proposed by Damga rd, Geisler, and Krøigaard (DGK protocol) [8] which takes inputs in binary and produces a list of intermediate results that are either encryption of zero or non-zero integer. Sec Comp directly produces an encrypted bit that is used for branching in decision tree evaluation. The extra interactions for processing the list of encrypted integers output by the DGK protocol (or other DGK-based protocols [9]) in the existing works are not necessary. It thus achieves a lower round complexity.


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