Privacy-Preserving Decision Trees Evaluation via Linear Functions

Tai, Raymond K. H.; Jack, P. K.; Zhao, Yongjun; Chow, Sherman S. M.

doi:10.1007/978-3-319-66399-9_27

Cited by 74 publications

(89 citation statements)

References 28 publications

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“…Existing privacy-preserving protocols [5]- [7] follow the clientserver 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 Damgård, 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.…”

Section: Current Solutionsmentioning

confidence: 99%

“…These state-of-the-art works [5]- [7] use additive homomorphic encryption (HE) and cannot be easily extended to work in a collaborative setting which naturally requires multiplication of two ciphertexts. A best-effort workaround is to have the client sends separate requests to each model owner, as illustrated in Fig.…”

Section: Current Solutionsmentioning

confidence: 99%

“…Tai et al [7] proposed the concept of path cost for transforming the tree into a set of linear equations which is compatible with efficient cryptographic operation. For each leaf node l k , it computes the sum of the boolean results b i along the path from the root to l k as the path cost pc k .…”

Section: Secure Evaluation Of Decision Treesmentioning

confidence: 99%

See 2 more Smart Citations

Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Aloufi

Wong

et al. 2019

IEEE Trans. Dependable and Secure Comput.

Self Cite

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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.

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Section: Current Solutionsmentioning

confidence: 99%

Section: Current Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Aloufi

Wong

et al. 2019

IEEE Trans. Dependable and Secure Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nikolaenko et al (2013) designed a privacy-preserving ridge regression on hundreds of data records, which can be regard as a building block for many machine learning operations. Raymond et al Tai et al (2017) studied privacy-preserving decision trees evaluation via linear functions, and more. Generally speaking, privacy-preserving machine learning can be roughly divided into two research goals, data perturbation and cryptographic tools.…”

Section: Related Workmentioning

confidence: 99%

Verifiable privacy-preserving single-layer perceptron training scheme in cloud computing

et al. 2018

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With the advent of artificial intelligence, machine learning has been well explored and extensively applied into numerous fields, such as pattern recognition, image processing and cloud computing. Very recently, machine learning hosted in a cloud service has gained more attentions due to the benefits from the outsourcing paradigm. Based on cloud-aided computation techniques, the heavy computation tasks involved in machine learning process can be off-loaded into the cloud server in a pay-per-use manner, whereas outsourcing large-scale collection of sensitive data risks privacy leakage since the cloud server is semi-honest. Therefore, privacy preservation for the client and verification for the returned results become two challenges to be dealt with. In this paper, we focus on designing a novel privacy-preserving single-layer perceptron training scheme which supports batch patterns training and verification for the training results on the client side. In addition, adopting classical secure two-party computation method, we design a novel lightweight privacy-preserving predictive algorithm. Both two participants learns nothing about other's inputs, and the calculation result is only known by one party. Detailed security analysis shows that the proposed scheme can achieve the desired security properties. We also demonstrate the efficiency of our scheme by providing the experimental evaluation on two different real datasets.

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“…Another interesting approach is machine learning classification over encrypted data [14], in which a private decision tree classifier allows the server to traverse a binary decision tree using the client's input, such that the server does not learn the input x and the client does not learn the structure of the tree and the thresholds at each node. A recent work [15] proposes privacy-preserving decision tree evaluation protocols which hide the sensitive inputs from the counterparty using an additively homomorphic encryption (AHE), which are similar to the ElGamal encryption procedure.…”

Section: Introductionmentioning

confidence: 99%

On Using Linear Diophantine Equations for in-Parallel Hiding of Decision Tree Rules

Feretzakis

Kalles

Verykios

2019

Entropy

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Data sharing among organizations has become an increasingly common procedure in several areas such as advertising, marketing, electronic commerce, banking, and insurance sectors. However, any organization will most likely try to keep some patterns as hidden as possible once it shares its datasets with others. This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. We adopt a record augmentation approach to hide critical classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or cryptographic techniques, which limit the usability of the data, since the raw data itself is readily available for public use. We propose a look ahead technique using linear Diophantine equations to add the appropriate number of instances while maintaining the initial entropy of the nodes. This method can be used to hide one or more decision tree rules optimally.

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Privacy-Preserving Decision Trees Evaluation via Linear Functions

Cited by 74 publications

References 28 publications

Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Verifiable privacy-preserving single-layer perceptron training scheme in cloud computing

On Using Linear Diophantine Equations for in-Parallel Hiding of Decision Tree Rules

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