Non-Interactive Private Decision Tree Evaluation

  title={Non-Interactive Private Decision Tree Evaluation},
  author={Anselme Tueno and Yordan Boev and Florian Kerschbaum},
In this paper, we address the problem of privately evaluating a decision tree on private data. This scenario consists of a server holding a private decision tree model and a client interested in classifying its private attribute vector using the server’s private model. The goal of the computation is to obtain the classification while preserving the privacy of both—the decision tree and the client input. After the computation, the client learns the classification result and nothing else, and the… 
SortingHat: Efficient Private Decision Tree Evaluation via Homomorphic Encryption and Transciphering
This work presents an e-cient non-interactive design of PDTE, that is based on FHE techniques, and proposes a fast homomorphic comparison function where one input can be in plaintext format and improves both the communication cost and the time complexity of transciphering.
Optimizing Secure Decision Tree Inference Outsourcing
A new system that allows highly efficient outsourcing of decision tree inference to the cloud is designed, implemented, and evaluated that significantly improves upon the state-of-the-art in the overall online end-to-end secure inference service latency at the cloud as well as the local-side performance of the model provider.
A Method for Securely Comparing Integers using Binary Trees
A new protocol for secure integer comparison which consists of parties having each a private integer and which outperforms the original DGK protocol of Damgård et al. and reduces the running time by at least 45%.
Efficient Secure Computation of Order-Preserving Encryption
A secure multiparty protocol that enables secure range queries for multiple users by allowing the equivalent of a public-key OPE, and applies this construction to different OPE schemes including frequency-hiding OPE and OPE based on an efficiently searchable encrypted data structure which can withstand many of the popularized attacks on OPE.
Let's Stride Blindfolded in a Forest: Sublinear Multi-Client Decision Trees Evaluation
A privacy-preserving decision-tree evaluation protocol purely based on additive homomorphic encryption, without introducing dummy nodes for hiding the tree structure, but it runs a secure comparison for each decision node, resulting in linear complexity.
Efficient Encrypted Inference on Ensembles of Decision Trees
This work proposes a framework to transfer knowledge extracted by complex decision tree ensembles to shallow neural networks (referred to as DTNets) that are highly conducive to encrypted inference and minimizes the accuracy loss by searching for the best DTNet architecture that operates within the given depth constraints.
PEGASUS: Bridging Polynomial and Non-polynomial Evaluations in Homomorphic Encryption
This work proposes a practical framework PEGASUS, which can efficiently switch back and forth between a packed CKKS ciphertext and FHEW ciphertexts without decryption, allowing us to evaluate arithmetic functions efficiently on the CKKs side, and to evaluate look-up tables on FHew cipher Texts.
Privacy Enhanced Decision Tree Inference
This paper describes an end-to-end approach to support privacyenhanced decision tree classification using IBM supported open-source library HELib and shows that a highly secure and trusted decision tree service can be enabled.


SoK: Modular and Efficient Private Decision Tree Evaluation
This work systematically review and analyse state-of-the-art protocols for the three phases of private decision tree evaluation protocols: feature selection, comparison, and path evaluation, and identifies novel combinations of these protocols that provide better tradeoffs than existing protocols.
Non-interactive and Output Expressive Private Comparison from Homomorphic Encryption
This paper considers a variant setting of a fully homomorphic encryption scheme in which the inputs a and b as well as the result bit 1 {a > b} are encrypted, giving about 48 - 90 fold speed up over previous solutions.
Private Evaluation of Decision Trees using Sublinear Cost
This paper is able to provide the first protocol with sublinear cost for large trees, but also reduce the communication cost for the large real-world data set “Spambase” from 18 MB to 1[triangleright]2 MB and the computation time from 17 seconds to less than 1 second in a LAN setting.
Optimized Search-and-Compute Circuits and Their Application to Query Evaluation on Encrypted Data
A unified framework to efficiently and privately process queries that require both search and compute operations to be performed on fully encrypted databases is constructed.
Privately Evaluating Decision Trees and Random Forests
Two protocols for privately evaluating decision trees and random forests are developed and an extension of the semi-honest protocol is given that is robust against malicious adversaries and demonstrates a tenfold improvement in computation and bandwidth.
Commodity-based cryptography (extended abstract)
A commoditybased model in which servers provide security resources to clients but are not involved in the clients’ computations themselves is proposed, which gracefully accommodates expansion without introducing bottlenecks (even polynomial) at larger scales.
Privacy-Preserving Decision Trees Evaluation via Linear Functions
This work proposes privacy-preserving decision tree evaluation protocols which hide the sensitive inputs (model and query) from the counterparty by cleverly exploiting the structure of decision trees, which avoids an exponential number of encryptions in the depth of the decision tree.
Multiparty Computation from Somewhat Homomorphic Encryption
We propose a general multiparty computation protocol secure against an active adversary corrupting up to $$n-1$$ of the n players. The protocol may be used to compute securely arithmetic circuits
Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models Based on Pre-Computation
This paper proposes a novel protocol for privacy-preserving classification of decision trees, a popular machine learning model in these scenarios, and presents accuracy and runtime results for seven classification benchmark datasets from the UCI repository.
Privacy-preserving remote diagnostics
An efficient protocol for privacy-preserving evaluation of diagnostic programs, represented as binary decision trees or branching programs, is presented, significantly more efficient than those obtained by direct application of generic secure multi-party computation techniques.